 Methodology article
 Open Access
Elastic SCAD as a novel penalization method for SVM classification tasks in highdimensional data
 Natalia Becker^{1}Email author,
 Grischa Toedt^{1},
 Peter Lichter^{1} and
 Axel Benner^{2}
https://doi.org/10.1186/1471210512138
© Becker et al; licensee BioMed Central Ltd. 2011
 Received: 3 September 2010
 Accepted: 9 May 2011
 Published: 9 May 2011
Abstract
Background
Classification and variable selection play an important role in knowledge discovery in highdimensional data. Although Support Vector Machine (SVM) algorithms are among the most powerful classification and prediction methods with a wide range of scientific applications, the SVM does not include automatic feature selection and therefore a number of feature selection procedures have been developed. Regularisation approaches extend SVM to a feature selection method in a flexible way using penalty functions like LASSO, SCAD and Elastic Net.
We propose a novel penalty function for SVM classification tasks, Elastic SCAD, a combination of SCAD and ridge penalties which overcomes the limitations of each penalty alone.
Since SVM models are extremely sensitive to the choice of tuning parameters, we adopted an interval search algorithm, which in comparison to a fixed grid search finds rapidly and more precisely a global optimal solution.
Results
Feature selection methods with combined penalties (Elastic Net and Elastic SCAD SVMs) are more robust to a change of the model complexity than methods using single penalties. Our simulation study showed that Elastic SCAD SVM outperformed LASSO (L_{1}) and SCAD SVMs. Moreover, Elastic SCAD SVM provided sparser classifiers in terms of median number of features selected than Elastic Net SVM and often better predicted than Elastic Net in terms of misclassification error.
Finally, we applied the penalization methods described above on four publicly available breast cancer data sets. Elastic SCAD SVM was the only method providing robust classifiers in sparse and nonsparse situations.
Conclusions
The proposed Elastic SCAD SVM algorithm provides the advantages of the SCAD penalty and at the same time avoids sparsity limitations for nonsparse data. We were first to demonstrate that the integration of the interval search algorithm and penalized SVM classification techniques provides fast solutions on the optimization of tuning parameters.
The penalized SVM classification algorithms as well as fixed grid and interval search for finding appropriate tuning parameters were implemented in our freely available R package 'penalizedSVM'.
We conclude that the Elastic SCAD SVM is a flexible and robust tool for classification and feature selection tasks for highdimensional data such as microarray data sets.
Keywords
 Support Vector Machine
 Feature Selection
 Feature Selection Method
 Youden Index
 Support Vector Machine Classification
Background
Classification and prediction methods play important roles in data analysis for a wide range of applications. Frequently, classification is performed on highdimensional data, where the number of features is much larger compared to the number of samples ('large p small n' problem) [1]. In those cases, classification by Support Vector Machines (SVM), originally developed by Vapnik [2], is one of the most powerful techniques. The SVM classifier aims to separate the samples from different classes by a hyperplane with largest margin.
Often we do not only require a prediction rule but also need to identify relevant components of the classifier. Thus, it would be useful to combine feature selection methods with SVM classification. Feature selection methods aim at finding the features most relevant for prediction. In this context, the objective of feature selection is threefold: (i) improving the prediction performance of the predictors, (ii) providing faster and more costeffective predictors, and (iii) gaining a deeper insight into the underlying processes that generated the data.
Three main groups of feature selection methods exist: filter, wrapper and embedded methods [1, 3–6]. Filter methods simply rank individual features by independently assigning a score to each feature. These methods ignore redundancy and inevitably fail in situations where only a combination of features is predictive. Also, if there is a preset limit on the number of features to be chosen (e.g. top 10 features), this limit is arbitrary and may not include all informative features. Because of these drawbacks, the filter methods are not included in this work.
Connecting filtering with a prediction procedure, wrapper methods wrap feature selection around a particular learning algorithm. Thereby, prediction performance of a given learning method assesses only the usefulness of subsets of variables. After a subset with lowest prediction error is estimated, the final model with reduced number of features is built [5]. However, wrapper methods have the drawback of high computational load, making them less applicable when the dimensionality increases. Wrapper methods also share the arbitrariness of filter methods in feature selection.
The third group of feature selection procedures are embedded methods, which perform feature selection within learning classifiers to achieve better computational efficiency and better performance than wrapper methods. The embedded methods are less computationally expensive and less prone to overfitting than the wrappers [7].
Guyon [1] proposed the recursive feature elimination (RFE) method, which belongs to the wrapper methods. RFE iteratively keeps a subset of features which are ranked by their contribution to the classifier. This approach is computationally expensive and selecting features based only on their ranks may not derive acceptable prediction rules.
An alternative to SVM with RFE is to use penalized SVM with appropriate penalty functions. Penalized SVM belongs to embedded methods and provides an automatic feature selection. The investigation of the widely used family of penalization functions such as LASSO, SCAD, Elastic Net [8–10] and a novel proposed penalty Elastic SCAD in combination with SVM classification, is the objective of the paper. The ridge penalty [4] corresponds to the ordinary SVM, which does not provide any feature selection, is used as reference with respect to prediction accuracy.
Although feature selection methods can be applied to any highdimensional data, we illustrate the use of these methods on microarray gene expression data due to their relevance in cancer research. Data from microarray experiments are usually stored as large matrices of expression levels of genes in rows and different experimental conditions in columns. Microarray technology allows to screen thousand of genes simultaneously. Detailed reviews on the technology and statistical methods often used in microarray analyses are presented in [11–13].
Since SVM is extremely sensitive to the choice of tuning parameters, the search for optimal parameters becomes an essential part of the classification algorithm [14]. The problem of choosing appropriate tuning parameters is discussed and an interval search technique from Froehlich and Zell [15] is proposed to use for SVM classification.
In this paper, we investigate the behaviour of feature selection SVM classifier techniques including commonly used penalization methods together with a novel penalization method, the Elastic SCAD. We compare them to SVM classification with and without recursive feature elimination (RFE [1]) for situations of 'large p small n' problems.
The RFE SVM is chosen as as a stateoftheart representative of feature selection methods in applications [16, 17].
A simulation study is designed to investigate the behaviour of different penalization approaches. Publicly available microarray data sets are chosen for illustration purposes as applications on real highdimensional data.
Methods
Support Vector Machines
Suppose a training data set with input data vector x_{ i } ∈ ℝ ^{ p } and corresponding class labels y_{ i } ∈ {1, 1}, i = 1,..., n is given. The SVM finds a maximal margin hyperplane such that it maximises the distance between classes. A linear hyperplane can always perfectly separate n samples in n + 1 dimensions. Since we can assume that highdimensional data with p ≫ n is generally linear separable [6], increasing complexity by using nonlinear kernels is usually not needed. Thus, we use a linear SVM model throughout the paper.
where w = (w_{1}, w_{2},..., w_{ p } ) is a unique vector of coefficients of the hyperplane with w_{2} = 1 and b denotes the intercept of the hyperplane. We use '·' to denote the inner product operator. The class assignment for a test data vector x_{test} ∈ R^{ p } is given by y_{test} = sign [f (x_{test})].
Soft margin SVM
where the set of indices of the support vectors S is determined by S := {i : α_{ i } > 0}.
The coefficient can be calculated from for any i with α_{ i } > 0. In praxis, an average of all solutions for is used for numerical stability.
SVM as a penalization method
where the loss term is described by a sum of the hinge loss functions l (y_{ i } , f (x_{ i } )) = [1  y_{ i } f (x _{ i })]_{+} = max(1  y_{ i } f (x _{ i }), 0) for each sample vector x _{ i }, i = 1,..., n. The penalty term is denoted as pen _{ λ } (w) and can have different forms:
Ridge penalty
The L_{2} penalty shrinks the coefficients to control their variance. However, the ridge penalty provides no shrinkage of the coefficients to zero and hence no feature selection is performed.
LASSO
As a result of singularity of the L_{1} penalty function, L_{1} SVM automatically selects features by shrinking coefficients of the hyperplane to zero.
However, the L_{1} norm penalty has two limitations. First, the number of selected features is bounded by the number of samples. Second, it tends to select only one feature from a group of correlated features and drops the others.
Fung and Mangasarian [19] have published a fast L_{1} SVM modification, the Newton Linear Programming Support Vector Machine (NLPSVM), which we use in our analyses.
Smoothly clipped absolute deviation penalty (SCAD)
where w_{ j } , j = 1,..., p are the coefficients defining the hyperplane and a > 2 and λ > 0 are tuning parameters. Fan and Li [21] showed that SCAD prediction is not sensitive to selection of the tuning parameter a. Their suggested value a = 3.7 is therefore used in our analyses.
The SCAD penalty corresponds to a quadratic spline function with knots λ at and a λ. For small coefficients w_{ j } , j = 1,..., p, SCAD yields the same behaviour as L_{1}. For large coefficients, however, SCAD applies a constant penalty, in contrast to L_{1}. This reduces the estimation bias. Furthermore, the SCAD penalty holds better theoretical properties than the L_{1} penalty [21].
Elastic Net
where λ_{1}, λ_{2} ≥ 0 are the corresponding tuning parameters.
Elastic SCAD
Fan and Li [21] demonstrated the advantages of the SCAD penalty over the L_{1} penalty. However, using the SCAD penalty might be too strict in selecting features for nonsparse data. A modification of the SCAD penalty analogously to Elastic Net could keep the advantages of the SCAD penalty, and, at the same time, avoid too restrictive sparsity limitations for nonsparse data.
λ_{1}, λ_{2} ≥ 0 are the tuning parameters. We expect that the Elastic SCAD will improve the SCAD method for less sparse data. According to the nature of the SCAD and L_{2} penalties, the Elastic SCAD should show good prediction accuracy for both, sparse and nonsparse data.
It can be shown that the combined penalty provides sparsity, continuity, and asymptotic normality when the tuning parameter for the ridge penalty converges to zero, i.e. λ_{2} → 0. The asymptotic normality and sparsity of Elastic SCAD leads to the oracle property in the sense of Fan and Li [21].
where λ_{1}, λ_{2} ≥ 0 are the tuning parameters.
Elastic SCAD SVM: Algorithm
By solving Eq. (9) the same problems as for SCAD SVM occur: the hinge loss function is not differentiable at zero and the SCAD penalty is not convex in w. The Elastic SCAD SVM objective function can be locally approximated by a quadratic function and the minimisation problem can be solved iteratively similar to the SCAD approach [10, 21].
where due to symmetrical nature of the SCAD penalty w_{ j }  is used instead of w_{ j } .
It can be shown that both approximations and their original functions have the same gradient at the point (b_{0}, w_{0}). Therefore, the solution of the local quadratic function corresponds approximately to the solution of the original problem.
The Elastic SCAD SVM can be implemented by the following iterative algorithm.
Step 1 Set k = 1 and specify the initial value (b^{(1)}, w^{(1)}) by standard L_{2} SVM according to Zhang et al. [10].
Step 2 Store the solution of the k th iteration: (b_{0}, w_{0}) = (b^{(k)}, w^{(k)}).
Step 3 Minimize Ã (b, w) by solving Eq. (11), and denote the solution as (b^{(k+1)}, w^{(k+1)}).
Step 4 Let k = k + 1. Go to step 2 until convergence.
If elements are close to zero, for instance, smaller than 10^{4}, then the j th variable is considered to be redundant and in the next step will be removed from the model. The algorithm stops after convergence of (b^{(k)}, w^{(k)}).
Choosing tuning parameters
All SVM problems with or without feature selection use one or two tuning parameters which balance the tradeoff between data fit and model complexity. Since these parameters are data dependent, finding optimal tuning parameters is part of the classification task.
Fixed grid search
Tuning parameters are usually determined by a grid search. The grid search method calculates a target value, e.g. the misclassification rate, at each point over a fixed grid of parameter values. This method may offer some protection against local minima but it is not very efficient. The density of the grid plays a critical role in finding global optima. For very sparse grids, it is very likely to find local optimal points. By increasing the density of the grid, the computation cost increases rapidly with no guaranty of finding global optima. The major disadvantage of the fixed grid approach lies in the systematic check of the misclassification rates in each point of the grid. There is no possibility to skip redundant points or to add new ones.
When more parameters are included in the model, the computation complexity is increased. Thus, the fixed grid search is only suitable for tuning of very few parameters.
Interval search
Froehlich and Zell [15] suggested an efficient algorithm of finding a global optimum on the tuning parameter space using a method called EPSGO (Efficient Parameter Selection via Global Optimisation).
The main idea of the EPSGO algorithm is to treat the search for an optimal tuning parameter as a global optimisation problem. For that purpose, the Gaussian Process model is learned from the points in the parameter space which have been already visited. Thereby, training and testing of the GP is very efficient in comparison to the calculation of the original SVM models. New points in the parameter space are sampled by using the expected improvement criterion as described in the EGO algorithm [23], which avoids stacking in local minima. The stopping criteria of the EPSGO algorithm is either convergence of the algorithm or no change of the optimum during the last ten iterations.
Stratified cross validation
Using kfold cross validation, the data set is randomly split into k disjoint parts of roughly equal size, usually k = 5 or k = 10. In addition, the data is often split in a way that each fold contains approximately the same distribution of class labels as the whole data set, denoted by stratified cross validation. For each subset, one fits the model using the other k  1 parts and calculates the prediction error of the selected k th part of the data.
The case k = n is called leave one out cross validation (LOO CV). The choice of k determines a tradeoff between bias and variance of the prediction error. Kohavi [24] showed that tenfold stratified cross validation showed better performance in terms of bias and variance compared to 10 < k < n. Hastie et al. [4] recommended to perform five or tenfold cross validation as a good compromise between variance and bias. We used both, five and tenfold stratified cross validation for simulation study and real applications, respectively.
In the next two sections the application of penalized SVM classification methods are compared. We used simulated and publicly available data to investigate the behaviour of different feature selection SVMs. For all comparisons the R packages "penalizedSVM" [25] and "e1071" [26] were used which are freely available from the CRAN http://cran.rproject.org/, R version 2.10.1. The R package "e1071" is a wrapper for the wellknown LIBSVM software [27]. We used five and tenfold stratified cross validation in combination with interval search for tuning parameters as described above.
Results and Discussion
Simulation study
Simulation design
A comprehensive simulation study evaluating the performance of four feature selection SVM classifiers, L_{1} SVM, SCAD SVM, Elastic Net SVM and Elastic SCAD SVM, was performed. We used the ordinary L_{2} SVM algorithm with a liner kernel as a reference for prediction accuracy.
Two independent data sets are simulated: a training set for building the classifier and a test set for estimating of the prediction errors of classifiers. First, the training data is generated, and the optimal tuning parameters are found using fivefold stratified cross validation according to the interval search approach [15]. Then, the classification hyperplane is computed using the estimated tuning parameters. Finally, application of the classification rule to the test data provides the prediction characteristics such as misclassification error, sensitivity and specificity.
where β = {β_{1},..., β_{p}} is a vector of coefficients of a classifier and u_{ i } are realisations of a variable following a U 0[1] distribution.
with equal numbers of positive and negative coefficients. The intersect β_{0} is set to zero.
where r denotes the number of relevant features. Using correlated blocks we investigate the ability of selecting correlated features, the so called grouping effect.
Optimal tuning parameters are found by an interval search in tuning parameter space using five fold cross validation. We select a large tuning parameter interval to be certain not to stick in local optima. The tuning parameter space for L_{1} and SCAD SVM is onedimensional with λ_{1} ∈ [λ_{1,min}, λ_{1,max}]. Elastic SCAD has two tuning parameters λ_{1}, λ_{2} ∈ [λ_{ l, min } , λ_{ l, max }], l = 1, 2. Elastic Net applies LARS paths. for fixed λ_{2} a λ_{1} path is calculated and the optimal λ_{1} is identified (for details refer to [17]). Thus, the optimal pair of parameter for Elastic Net was found in the twodimensional space ℝ × [λ_{ l, min }, λ_{ l, max }] We set the search interval for both parameters to [λ_{ l, min }, λ_{ l, max }] = [2^{10}, 2^{10}], l = 1, 2.
The maximal Youden index is one, when the true positive rate is one and the false positive rate is zero. For a random classifier the expected Youden index is zero. The sensitivity and specificity have equal weights in this index. Most often the costs and consequences of true positives and false positives will differ greatly. Therefore, Gu and Pepe [29] recommend reporting the two measures separately. For our simulated data, we consider the Youden index to be an appropriate indicator for feature selection methods performance, since we weight errors equally.
It is worth to mention, that for discrete classier the Youden index and the area under the curve (AUC) provide the same message due to their linear relationship. According to Greiner et al. [30], if there is only one point in the ROC plot, the ROC curve is estimated by connecting the three points. the point corresponds to the classifier, the (0, 0) and (1, 1) edges of the plot. Then geometrically, the estimated AUC corresponds to the average of estimated sensitivity and specificity. Thus, the Youden index and the AUC have a linear relationship. AUC = (sensitivity + specificity)/2 = (Youden index +1)/2. Optimizing the AUC will lead to the same results as optimizing the Youden index when dealing with discrete classifiers. Nevertheless, for real data application, the AUC values are presented in a separate column due to higher level of familiarity in bioinformatics.
Finally, the misclassification rate, size of the classifiers and frequencies of the selected features within 100 simulation runs are computed.
Simulation results
The performance of the feature selection methods applied to simulated data using p = 1000 features and n = 500 samples for training and testing is presented in the next section. The percentage of relevant features varies between 1% and 20% in four steps, i.e. r = 10, 50, 100, 200. We assume to have correlated blocks of features as described in the design section. The optimal tuning parameters were chosen as described above. Multiple comparisons in performance measures between the proposed prediction methods and the best method (the MCB test) for each simulation step will be done according to Hsu [31] based on 100 simulation runs. We used a noninferiority margin of a procedure to distinguish methods with similar performance.
Misclassification rate
Mean misclassification rate of feature selection methods applied to simulated test data
FS method  r = 10  r = 50  r = 100  r = 200 

L_{2} SVM  34.8_{(2.2)}  33.1_{(2.0)}  33.3 _{(2.1)}  32.8 _{(1.9)} 
L_{1} SVM  28.3_{(2.8)}  28.6 _{(3.0)}  32.4 _{(2.2)}  32.9 _{(2.1)} 
SCAD SVM  18.0 _{(2.2)}  27.2 _{(4.4)}  35.3 _{(3.4)}  34.7 _{(4.1)} 
Elastic Net SVM  19.4 _{(2.0)}  24.7 _{(3.0)}  31.3 _{(2.3)}  33.1 _{(2.7)} 
Elastic SCAD SVM  20.8 _{(4.5)}  26.8 _{(4.2)}  33.1 _{(2.7)}  34.2 _{(4.1)} 
Youden index
Average Youden index for classifiers applied to simulated test data
FS method  r = 10  r = 50  r = 100  r = 200 

L_{1} SVM  0.81_{(0.11)}  0.59 _{(0.12)}  0.32_{(0.16)}  0.14_{(0.10)} 
SCAD SVM  0.96 _{(0.06)}  0.65 _{(0.12)}  0.28_{(0.12)}  0.16 _{(0.07)} 
Elastic Net SVM  0.95 _{(0.04)}  0.71 _{(0.09)}  0.48 _{(0.07)}  0.27 _{(0.05)} 
Elastic SCAD SVM  0.92 _{(0.11)}  0.67 _{(0.13)}  0.42 _{(0.09)}  0.27 _{(0.06)} 
All methods except the L1 SVM provided significantly comparable Youden indexes at the level α = 0.05 and a relevant difference Δ = 0.10 for r = 10. By increasing model complexity, the Elastic Net SVM showed the best Youden Index among all feature selection methods, closely followed by the Elastic SCAD SVM. Starting from r > 100 the is no significant difference between Elastic Net and Elastic SCAD SVMs. With increasing number of relevant features, the Youden index decreases from 0.9 to 0.27 for 'elastic' methods to 0.14 for the L_{1} SVM and to 0.16 for the SCAD SVM. respectively.
Sparsity of the classifier
Median number of features selected
FS method  r = 10  r = 50  r = 100  r = 200 

L_{1} SVM  141_{(56)}  296_{(98)}  509_{(290)}  789_{(223)} 
SCAD SVM  12 _{(3)}  61 _{(24)}  593_{(382)}  726_{(181)} 
Elastic Net SVM  38_{(25)}  242_{(110)}  355 _{(164)}  511 _{(183)} 
Elastic SCAD SVM  24_{(19})  161 _{(139)}  430 _{(116)}  493 _{(124)} 
Selection Frequencies
A frequencies plot for the simulation study is represented in 'Additional file 1  Frequencies plot'. With increasing number of relevant features (r), a decrease of the proportion of true positives (in red) and an increase of the proportion of false positives (in blue) for all feature selection models was observed, respectively. At the same time we observed an increase of the false positives, which are correlated with the true positives (in green) in classifiers.
The percentage of true positives in the classifiers is shown in Table S1 (Additional file 2  Tables S1, S2, S3). For r = 10 relevant features the Elastic Net SVM found almost all true positives (99.8%), followed by the Elastic SCAD SVM with 97.6%. For r = 50 the Elastic SCAD SVM achieved the sparsest solution followed by the L_{1} SVM. In less sparse models, the L_{1} SVM showed highest true positive rates of 84.5% and 86%.
Grouping effect
We further evaluated the ability of feature selection methods to select correlated features of true positives. Although for all scenarios L_{1} SVM has found the largest percentage of correlated features, which increases with increasing number of relevant features (23.6  62.5%), the level of correlated features is comparable to the level of nonrelevant features (Table S2).
Comparing Tables S1, S2 and S3 one can observe that the SCAD and the L_{1} SVMs failed to find features highly correlated with true positives more often than with independent false positives. The Elastic Net and the Elastic SCAD SVMs managed to discover correlated features (in green) more often than the independent false positives (in blue), at least for sparse models (r = 10 and r = 50).
The false positive rate
For very sparse models, the false positive rate (FPR) was the smallest for the SCAD SVM, followed by the Elastic Net and the Elastic SCAD SVMs (Table S3). For other less sparse models the Elastic Net SVM selected fewer false positives than the remaining methods. The second best method is the Elastic SCAD SVM.
Conclusions

As expected from theory the SCAD SVM and the L_{1} SVM produced classifiers with low prediction error for very sparse situations.

For less sparse and nonsparse models, the Elastic Net and the Elastic SCAD SVM showed better results than the L_{1} and the L_{2} SVMs with respect to accuracy, Youden index and sparsity of classifiers.

The SCAD SVM and the L_{1} SVM were not able to find correlated features. The Elastic Net and the Elastic SCAD SVMs found correlated features more frequently than one would expected under random selection. Although the grouping effect strength weakens with increasing number of relevant features, the Elastic Net and Elastic SCAD SVMs still managed the grouping effects.

In general, the Elastic Net and the Elastic SCAD SVMs showed similar performance. Additionally, the Elastic SCAD SVM provided more sparse classifiers than the Elastic Net SVM.
Applications
NKI breast cancer data set
Two studies on breast cancer from the Netherlands Cancer Institute (NKI) were published by the van't Veer group [32], [33]. In the first paper, a set of 78 lymph node negative patients with preselected 4919 clones were used to find a predictor for distant metastases. The classifier was trained and validated on patients who developed distant metastases within five years after surgery and patients being metastasisfree for at least the first five years. The resulting predictor was a 70gene signature also known as MammaPrint(R). We will use the MammaPrint(R) signature as reference in the analysis of the NKI breast cancer data set. The signature was generated based on genewise correlations between the gene expression and metastasis occurrence. The data set was taken from http://www.rii.com/publications/2002/vantveer.html.
In a subsequent validation study, data from 295 patients (which partially included patients from the first study) were used to validate the signature [33]. Among the patients, 151 were lymph node negative and 144 had lymph node positive disease. The preprocessed data containing 4919 clones is available from http://www.rii.com/publications/2002/nejm.html.
After excluding patients being identical to the training set and 10 patients with no metastasis information, 253 patients remained. Among the 253 patients there are 114 lymph node negative and 139 lymph node positive patients.
In this paper, we combined the 78 lymph node negative sample set from the first publication with 114 lymph node negative patients from the validation study. In total, a data set with 192 lymph node negative samples was used. The estimation of classifier performance was computed by a tenfold stratified crossvalidation.
Results on NKI breast cancer data set
Summary of classifiers for the NKI data set with distant metastasis as endpoint
FS method  # features  test error(%)  sensitivity(%)  specificity(%)  Youden index  AUC 

L_{2} SVM  4919 (all)  24  79  68  0.47  0.735 
RFE SVM  256  25  83  59  0.42  0.71 
MammaPrint(R)  70  37  74  40  0.14  0.57 
L_{1} SVM  1573  17  84  81  0.65  0.825 
SCAD SVM  476  25  84  56  0.39  0.695 
Elastic Net SVM  109  25  83  59  0.42  0.71 
Elastic SCAD SVM  459  24  84  57  0.41  0.705 
RFE SVM was used according to Guyon's approach [1], where at each iteration half of features with lowest ranks are eliminated. To increase the classifier's stability, RFE SVM with fivefold stratified cross validation was repeated five times. According to the average cross validation error the optimal number of features was 2^{8} = 256. Optimal tuning parameters for penalized SVM methods were found by the interval search on the tuning parameter space as described in the method section using tenfold stratified cross validation.
The SCAD SVM reduced the number of features from 4919 to 476, L_{1} SVM selected 1573 features, Elastic Net 109 features, and the Elastic SCAD had 459 features in the classifier. For the NKI data set the best predictor with respect to misclassification error was L_{1} SVM. Elastic Net and Elastic SCAD SVMs provided similar results, followed by SCAD SVM, which was slightly worse.
Interestingly, the MammaPrint(R) signature ("70_sign") neither showed good test accuracy nor a reliable sensitivity or specificity. L_{2} SVM and the feature selection methods outperformed the published signature.
Conclusions
For the two data sets from van't Veer group feature selection methods produced signatures with similar prediction accuracy, but being different in size. L_{1} SVM with a nonsparse classifier provided the best sensitivity and specificity, followed by more sparse predictors from Elastic Net SVM and Elastic SCAD SVM.
MAQCII breast cancer data set
This data set is part of the MicroArray Quality Control (MAQC)II project, which has been designed to investigate numerous data analysis methods and to reach consensus on the "best practices" for development and validation of microarraybased classifiers for clinical and preclinical applications. One biological endpoint is estrogen receptor (ER) status. Out of 230 patients in total, 89 patients have negative ER status and 141 patients positive ER status. A clinical endpoint is pathological complete response (pCR) to preoperative chemotherapy. Among the 230 patients in the data set, 182 patients showed no pCR and 48 had a pCR.
The preprocessed data contains 22283 features and is available from GEO database, accession number GSE20194.
Results on MAQCII breast cancer data set
The feature selection methods SCAD SVM, L_{1} SVM, Elastic Net SVM and Elastic SCAD SVM with internal tenfold stratified cross validation were applied to build classifiers. Additionally, the L_{2} SVM and the RFE SVM were used as reference models. To achieve performance measurements tenfold stratified cross validation was used.
pCR prediction
Summary of classifiers for the MAQCII data set with pCR status as endpoint
FS method  # features  test error(%)  sensitivity(%)  specificity(%)  Youden index  AUC 

L_{2} SVM  22283 (all)  19  32  97  0.25  0.62 
RFE SVM  2048  20  27  93  0.20  0.895 
L_{1} SVM  7299  21  27  93  0.20  0.60 
SCAD SVM  1072  21  35  91  0.26  0.63 
Elastic Net SVM  398  24  15  91  0.06  0.53 
Elastic SCAD SVM  148  15  52  94  0.46  0.73 
Overall, Elastic SCAD showed better classification characteristics than other methods. Moreover, the higher specificity of the Elastic SCAD classifier is of clinical importance. The patients that did not respond to the therapy were recognized with higher probability.
ER status
Summary of classifiers for the MAQCII data set with ER status as endpoint
FS method  # features  test error(%)  sensitivity(%)  specificity(%)  Youden index  AUC 

L_{2} SVM  22283 (all)  10  93  84  0.77  0.855 
RFE SVM  2048  14  89  81  0.79  0.895 
L_{1} SVM  860  11  89  88  0.77  0.885 
SCAD SVM  32  9  91  91  0.83  0.915 
Elastic Net SVM  3  9  93  82  0.75  0.875 
Elastic SCAD SVM  59  7  96  88  0.84  0.92 
For this classification task, the sparse classifier Elastic SCAD and SCAD showed the best characteristics.
Screening on two additional breast cancer data sets
Summary of classifiers for Mainz cohort, validated on Rotterdam cohort with relapse as endpoint
FS method  # features  test error(%)  sensitivity(%)  specificity(%)  Youden index  AUC 

L_{2} SVM  22283 (all)  44  68  48  0.16  0.58 
RFE SVM  512  37  38  77  0.16  0.58 
L_{1} SVM  1861  37  47  72  0.19  0.595 
SCAD SVM  915  37  35  80  0.15  0.575 
Elastic Net SVM  278  43  51  60  0.12  0.56 
Elastic SCAD SVM  2823  37  34  81  0.15  0.575 
Summary of classifiers for Rotterdam cohort, validated on Mainz cohort with relapse as endpoint
FS method  # features  test error(%)  sensitivity(%)  specificity(%)  Youden index  AUC 

L_{2} SVM  22283 (all)  25  11  93  0.04  0.52 
RFE SVM  22283 (all)  25  11  93  0.04  0.52 
L_{1} SVM  8319  28  30  84  0.14  0.57 
SCAD SVM  1284  35  41  72  0.13  0.565 
Elastic Net SVM  272  28  37  81  0.19  0.595 
Elastic SCAD SVM  2074  26  30  87  0.17  0.585 
We can see that all feature selection methods had lower misclassification test error than the L_{2} SVM containing all features for breast cancer data sets. The classifiers perform different for each data set. The Elastic Net SVM had small error rate for the Rotterdam cohort, but failed to classify the Mainz samples adequately. The L_{2} SVM classifier including all features had the second best Youden index for the Mainz set, however for Rotterdam data showed the worst Youden index. Using both, the test error and AUC value as a combined measure of sensitivity and the specificity, one would conclude that the L_{1}, SCAD and Elastic SCAD SVMs provide reasonable and robust solutions with respect to the combined analysis of the two breast cancer data sets.
Altogether, Elastic SCAD seems to provide an overall acceptable compromise for sparse and nonsparse data.
Conclusions
In highdimensional prediction tasks, feature selection plays an important role. In this paper, we proposed a novel feature selection method for SVM classification using a combination of two penalties, SCAD and L_{2}. The commonly used penalty functions L_{1}, SCAD and Elastic Net were investigated in parallel with the new method on simulated and public data. To address the problem of finding optimal tuning parameters for SVM classification the efficient parameter search algorithm from Froehlich and Zell [15] was implemented.
In almost all cases, the four feature selection classifies outperformed ordinary Support Vector Classification using the L_{2} penalty. From the simulation study we concluded that for sufficiently large sample sizes, feature selection methods with combined penalties are more robust to changes of the model complexity than using single penalties alone.
The SCAD SVM followed by the L_{1} SVM, as expected, showed very good performance in terms of prediction accuracy for very sparse models, but failed for less sparse models. Combined penalty functions in combination with the SVM algorithm, Elastic Net and Elastic SCAD, performed well for sparse and less sparse models.
Comparisons with commonly used penalty functions in the simulation study illustrated that the Elastic SCAD and the Elastic Net SVMs showed similar performance with respect to prediction accuracy. Both 'elastic' methods were able to consider correlation structures in the input data (grouping effect). However, the Elastic SCAD SVM in general provides more sparse classifiers than the Elastic Net SVM.
Finally, applied to publicly available breast cancer data sets, the Elastic SCAD SVM performed very flexible and robust in sparse and nonsparse situations. Results from the simulation study and real data application render Elastic SCAD SVM with automatic feature selection a promising classification method for highdimensional applications.
Declarations
Acknowledgements
We would like to thank the editor and the anonymous reviewers for their constructive comments, which significantly improved the quality of this manuscript.
This work was supported by Grant 01GS0883 from the German Federal Ministry of Education and Research (BMBF) within the National Genome Research Network NGFNplus.
Authors’ Affiliations
References
 Guyon I, Weston J, Barnhill S, Vapnik V: Gene Selection for Cancer Classification using Support Vector Machines. Machine Learning 2002, 44(3):438–443.Google Scholar
 Vapnik V: The Nature of Statistical Learning Theory. New York: Springer; 1995.View ArticleGoogle Scholar
 Kohavi R, John GH: Wrappers for feature subset selection. Artificial Intelligence 1997, 273–324.Google Scholar
 Hastie T, Tibshirani R, Friedman J: The elements of statistical learning: data mining inference and prediction. New York: Springer; 2001.View ArticleGoogle Scholar
 Inza I, Sierra B, Blanco R, Larranaga P: Gene selection by sequential search wrapper approaches in microarray cancer class prediction. ournal of Intelligent and Fuzzy Systems 2002, 12: 25–33.Google Scholar
 Markowetz F, Spang R: Molecular diagnosis: classification, model selection and performance evaluation. Methods Inf Med 2005, 44(3):438–443.PubMedGoogle Scholar
 Guyon I, Elisseff A: An Introduction to Variable and Feature Selection. Journal of Machine Learning Research 2003, 3: 1157–1182. 10.1162/153244303322753616Google Scholar
 Tibshirani R: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B 1996, 58: 267–288.Google Scholar
 Zou H, Hastie T: Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, Series B 2005, 67(2):301–320. 10.1111/j.14679868.2005.00503.xView ArticleGoogle Scholar
 Zhang HH, Ahn J, Lin X, Park C: Gene selection using support vector machines with nonconvex penalty. Bioinformatics 2006, 22(1):88–95. 10.1093/bioinformatics/bti736View ArticlePubMedGoogle Scholar
 Allison DB, Cui X, Page GP, Sabripour M: Microarray data analysis: from disarray to consolidation and consensus. Nature Reviews Genetics 2006, 7: 55–65. 10.1038/nrg1749View ArticlePubMedGoogle Scholar
 Hoheisel JD: Microarray technology: beyond transcript profiling and genotype analysis. Nature Reviews Genetics 2006, 7: 200–210. 10.1038/nrg1809PubMedGoogle Scholar
 Quackenbush J: Computational analysis of microarray data. Nature Review Genetics 2001, 2: 418–427. 10.1038/35076576View ArticleGoogle Scholar
 Li X, Xu R: High Dimensional Data Analysis in Oncology. New York: Springer; 2008.Google Scholar
 Froehlich H, Zell A: Efficient parameter selection for support vector machines in classification and regression via modelbased global optimization. In Proc Int Joint Conf Neural Networks 2005, 1431–1438.Google Scholar
 Liu Q, Sung AH, Chen Z, Liu J, Huang X, Deng Y: Feature selection and classification of MAQCII breast cancer and multiple myeloma microarray gene expression data. PLoS ONE 2009, 4: e8250. 10.1371/journal.pone.0008250PubMed CentralView ArticlePubMedGoogle Scholar
 Wang L, Zhu J, Zou H: Hybrid huberized support vector machines for microarray classification and gene selection. Bioinformatics 2008, 24(3):412–419. 10.1093/bioinformatics/btm579View ArticlePubMedGoogle Scholar
 Bradley PS, Mangasarian OL: Feature selection via concave minimization and support vector machines. Machine Learning Proceedings of the Fifteenth International Conference 1998, 82–90.Google Scholar
 Fung G, Mangasarian OL: A feature selection newton method for support vector machine classification. Computational Optimization and Applications Journal 2004, 28(2):185–202.View ArticleGoogle Scholar
 Comments onWavelets in Statistics. A Review by Antoniadis 1997.Google Scholar
 Fan J, Li R: Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of American Statistical Association 2001, 96: 1348–1360. 10.1198/016214501753382273View ArticleGoogle Scholar
 Wang L, Zhu J, Zou H: The double regularized support vector machine. Statistica Sinica 2006, 16: 589–615.Google Scholar
 Jones D, Schonlau M, Welch W: Efficient global optimization of expensive blackbox functions. Journal of Global Optimization 1998, 13: 455–492. 10.1023/A:1008306431147View ArticleGoogle Scholar
 Kohavi R: A study of crossvalidation and bootstrap for accuracy estimation and model selection. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence 1995, 2: 1137–1143.Google Scholar
 Becker N, Werft W, Toedt G, Lichter P, Benner A: penalizedSVM: a Rpackage for feature selection SVM classification. Bioinformatics 2009, 25(13):1711–1712. 10.1093/bioinformatics/btp286View ArticlePubMedGoogle Scholar
 Dimitriadou E, Hornik K, Leisch F, Meyer D, Weingessel A:e1071: Misc Functions of the Department of Statistics (e1071), TU Wien. 2009. [R package version 1.5–22] [http://CRAN.Rproject.org/package=e1071] [R package version 1.522]Google Scholar
 Chang C, Lin C: LIBSVM: a Library for Support Vector Machines.2001. [http://www.csie.ntu.edu.tw/~cjlin/libsvm]Google Scholar
 Storey JD, Tibshirani R: Estimating false discovery rates under dependence, with applications to DNA microarrays. Tech rep., Stanford University: Stanford Technical Report; 2001.Google Scholar
 Gu W, Pepe M: Measures to Summarize and Compare the Predictive Capacity of Markers. Working paper 342., UW Biostatistics Working Paper Series 2009. [http://www.bepress.com/uwbiostat/paper342]Google Scholar
 Greiner M, Pfeiffer D, Smith RD: Principles and practical application of the receiveroperating characteristic analysis for diagnostic tests. Preventive Veterinary Medicine 2000, 45(1–2):23–41. [http://www.sciencedirect.com/science/article/B6TBK408BJCN3/2/3a4753dc80ec448666ef990ee4c33078] 10.1016/S01675877(00)00115XView ArticlePubMedGoogle Scholar
 Hsu JC: Multiple Comparisons Theory and Methods. Chapman & Hall; 1996.View ArticleGoogle Scholar
 van't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao HL, Mand Peterse, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts PS, Cand Linsley, Bernards R, H FS: Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 415: 530–536. 10.1038/415530aView ArticleGoogle Scholar
 van de Vijver MJ, He YD, van't Veer LJ, Dai H, Hart AA, Voskuil DW, Schreiber GJ, Peterse HL, Roberts C, Marton MJ, Parrish M, Atsma D, Witteveen A, Glas A, Delahaye L, van der Velde T, Bartelink H, Rodenhuis S, Rutgers ET, Friend SH, Bernards R: A geneexpression signature as a predictor of survival in breast cancer. N Engl J Med 2002, 347: 1999–2009. 10.1056/NEJMoa021967View ArticlePubMedGoogle Scholar
 Johannes M, Brase JC, Froehlich H, Gade S, Gehrmann M, Faelth M, Sueltmann H, Beissbarth T: Integration Of Pathway Knowledge Into A Reweighted Recursive Feature Elimination Approach For Risk Stratification Of Cancer Patients. Bioinformatics 2010, 26(17):2136–2144. 10.1093/bioinformatics/btq345View ArticlePubMedGoogle Scholar
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