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Learning misclassification costs for imbalanced classification on gene expression data
BMC Bioinformatics volume 20, Article number: 681 (2019)
Abstract
Background
Costsensitive algorithm is an effective strategy to solve imbalanced classification problem. However, the misclassification costs are usually determined empirically based on user expertise, which leads to unstable performance of costsensitive classification. Therefore, an efficient and accurate method is needed to calculate the optimal cost weights.
Results
In this paper, two approaches are proposed to search for the optimal cost weights, targeting at the highest weighted classification accuracy (WCA). One is the optimal cost weights grid searching and the other is the function fitting. Comparisons are made between these between the two algorithms above. In experiments, we classify imbalanced gene expression data using extreme learning machine to test the cost weights obtained by the two approaches.
Conclusions
Comprehensive experimental results show that the function fitting method is generally more efficient, which can well find the optimal cost weights with acceptable WCA.
Background
Classification of gene expression data reveals tremendous information in various application fields of biomedical research, such as cancer diagnosis, prognosis and predictions [1,2,3]. However, the gene expression data is composed of highdimensional, noisy and imbalanced data samples [4]. The characteristic of imbalanced data is serious imbalance in the proportion of positive and negative samples [5, 6]. Gene expression data exacts a series of preprocessing steps to eliminate misleading classification results [7]. Moreover, the classification of gene expression data is a costsensitive problem, although both positive and negative classifications of cancer genes provide important evidences for doctors to make the treatment plan.
Traditional machine learning algorithms usually assume that the training set is balanced. For imbalanced datasets, such as the gene expression datasets, the classical classification algorithms with the correct classification rates (CCR) may bias towards the majority classes. However, the misclassifications of minority classes usually contribute the higher influences than those of majority classes. Therefore, The introduction of cost sensitive learning (CSL) is necessary to eliminate the defects of traditional classification algorithms for imbalanced datasets. Traditionally, oversampling the minority class, undersampling the majority class, and synthesizing new minority classes can be used to handle this problem. In this work, we utilize a more sophisticated way to search for the optimal weights, and the proposed methods are more advanced than ever.
In CSL, misclassification cost is an important factor to evaluate the classification performance of imbalanced datasets. However, solving the misclassification cost matrix is not a trivial task in many situations [8,9,10]. A direct solution for finding the misclassification costs is to assign them manually according to user expertise or inversely calculate the costs based on class distribution [11,12,13]. More sophisticated solutions can be found by fitting the importance of features to adaptive equations.
In this paper, we learn the misclassification cost from the evaluation functions of costsensitive algorithms, using weighted classification accuracy as the measurement of costsensitive classification performance. The cost weights that lead to optimal classification performance are learned by grid searching strategy. It will help the researchers to obtain a reference weight. Then, three fitting functions will be found to represent the optimal cost weights. A series of comprehensive experimental results show that the function fitting approach is an effective way of finding the optimal cost weights, targeting at high weighted classification accuracy (WCA). Fitting functions can accurately locate optimal weights. Appropriate weights will greatly improve the accuracy of the model.
Imbalanced data greatly affects the accuracy of classification. We discuss the costsensitive classification algorithms in the imbalance problem. CSL is one of the most hot topics in the field of machine learning. Many works have studied on CSL and embedded the misclassification costs into various classifiers, such as the decision trees (DTs), support vector machines (SVMs) and extreme learning machines (ELMs). Chai et al. [14] considered the testing costs of missing values in naive Bayes (NB) and DT algorithms. Feng [15] defined a customized objective function for misclassification costs and designed a score evaluation based costsensitive DT. For multiclass classification problems, Feng’s method generally achieves higher classification accuracy or lower misclassification costs. Zhao and Li [16] extended the evaluation function by including weighted information gain ratio and the test cost for the costsensitive DT. The proposed costsensitive DT algorithm not only reduced the misclassification cost, but also improved the classification efficiency of the original C4.5 algorithm [17, 18]. Lu et al. [19] made use of the costsensitive DTs as base classifiers and constructed a costsensitive rotational forest. Two kinds of DTs, i.e., EG2 and C4.5, are considered and tested [20]. These experiments show that integrating costsensitive to classification algorithms can effectively improve classification efficiency.
Cost sensitivity and classification algorithms combine to form efficient classification methods. Cao et al. [21] proposed to embed evaluation measures into the objective function for to improve the performance of a costsensitive support vector machine (CSSVM). He et al. [22] integrated the Gaussian Mixture Model (GMM) into the CSSVM to deal with the imbalanced classification problem. Cheng and Wu [23] added weights to features and introduced a weighted features costsensitive SVM (WFCSSVM). The WFCSSVM algorithm showed significant performance improvement on both aspects of accuracy and cost. Silva et al. [24] combined CSSVM with semisupervised learning method to form a hybrid classification algorithm. The effectiveness of the proposed hybrid method is shown in the experimental results on Earth monitoring and landscape mapping. Cao et al. [25] tackled the problem of multilabeled imbalanced data classification problem. They successfully assigned different misclassification costs to different label sets for reducing the overall misclassification cost.
CSELM has been studied by many researchers in various aspects. Zong et al. [26] introduced a weighted extreme learning machine (WELM) for imbalanced data learning. It was claimed that the WELM can be extended to a costsensitive ELM (CSELM). Zheng et al. [27] formally applied the concept of the costsensitivity to extreme learning machine (ELM). Yan et al. [28, 29] extended Zheng et al.’s work and introduced a costsensitive dissimilar ELM (CSDELM). Compared to traditional ELM algorithms, the CSELM algorithms guarantee the classification accuracy and reduce the misclassification cost. More recently, Zhang and Zhang [30] solved the problem of defining and optimizing the cost matrix for CSELM to make it more robust and stable [31, 32]. Zhu and Wang [33] treated CSELM as a base classifier to solve a semisupervised learning problem. Incremental results show that the CSELM has better performance in terms of accuracy, cost, efficiency and robustness over other existing classifiers.
Classical definition of cost matrix
Considering the binary classification problem, the confusion matrix shows four types of classification results according to the prediction values, namely, true positive, false positive, false negative and true negative (Table 1) [34, 35].
The CSL seeks the overall minimum cost by introducing sensitive costs, rather than only aiming at high CCR. While there are several types of classification costs, it should be noted that this work only focuses on the misclassification cost.
Misclassification cost can be viewed as penalties for errors in the classification process. In binary classification problems, costs caused by different types of errors may be different. We define the minority class as positive (P), the majority class as negative (N), and construct the cost matrix C as shown in Table 2.
In Table 2, C_{00} and C_{11} show the cost of correct classification. By default, we set the costs of correct classifications as 0. C_{01} and C_{10} show the costs of error classifications, where C_{01} denotes the misclassification costs of samples from P class, and C_{10} denotes the misclassification costs of samples from N class. Therefore, the cost matrix in Table 2 can be simplified as:
Correct classification rates versus weighted classification accuracy
For classical machine learning problems, the classification accuracy always refers to the correct classification rate (CCR) [36,37,38], or called overall accuracy (OA) [39,40,41,42], which is the proportion of all correctly classified samples:
However, for imbalanced datasets where the numbers of positive and negative samples differ significantly, the CCR might be misleading [43, 44]. Considering a test set containing 99 negative samples but with only one positive sample [45, 46], a poorly designed classifier that simply puts all samples as negative will achieve an overall accuracy of 99/100 = 0.99, even though the accuracy for positive class is 0. To resolve this issue, we introduce the notion of adaptive classification accuracy (ACA) defined as follows:
By embedding a weight w_{i} into the ith class, we get the weighted classification accuracy (WCA) as:
By enforcing w_{1} + w_{2} = 1, Formula (9) is reduced to:
Formula (10) can be easily extended to multiclassification problems:
where n denotes the number of classes, M_{i} (i = 1, 2,..., n) denotes the number of samples belonging to the ith class, and CM_{i} (i = 1, 2,..., n) denotes the number of correctly classified samples within ith class. Since the WCA is more accurate describing the classification accuracy, we use the WCA to evaluate the classification performance of costsensitive classifiers in the problem of gene expression data classification.
Methods
Optimal cost weights searching
From the University of California Irvine (UCI) standard classification dataset, we choose Leukemia, Colon, Prostate, Lung and Ovarian gene as the datasets for cost weights searching and further test, i.e., the Leukemia cancer dataset, the Colon cancer dataset, the Prostate cancer dataset, the Lung cancer dataset, and the Ovarian cancer in the tumor data respectively. All details of aforementioned datasets are shown in Table 3.
Optimal cost weights searching by grid searching strategy
The optimal weights are searched by an adaptive algorithm using grid searching. There are two crucial factors to consider: the sample importance w and sample categorical distribution p. The sample categorical distribution p is the proportion between the number of positive class and negative class in test sets. Test set is constructed by random sampling. As such, it is necessary to study the relationship between the three factors, namely, w, p and WCA, where WCA is the fitness value for the grid searching strategy. In general, the grid searching strategy can be described as follows (the detailed algorithm steps are listed in Table 4):
1) Set the searching region as M, grid searching step size as T, and the initial position as P_{0};
2) Calculate the fitness of the current position, record the position P_{max} that has the best fitness f_{max} (f_{max} = WCA);
3) Update current location, P=P + T;
4) if the current fitness value is greater than f_{max}, update f_{max} and P_{max};
5) return f_{max} and P_{max.}
Extreme learning machine is an effective single hiddenlayer feedforward neural network (SLFN) learning algorithm. Costsensitive extreme learning machine (CSELM) is a kind of ELM, which attaches a cost matrix on output layer. In this research, we set the number of hidden neurons at 10. Less neurons will make the result more sensitive to observe the change of weights. And seven different gene expression datasets are used to obtain the classification results with CSELM as the classifier. CSELM minimizes the conditional risk by embedding misclassification cost in ELM.
where R(ix) is the conditional risk when the sample x is assigned to the class i, and P(jx) is the conditional probability that x belongs to j, C(i, j) is the risk of misclassifying j to class i, where i, j ∈ {c_{1}, c_{2}, …, c_{m}} and m is the number of classification categories.
Results
Optimal cost weights searching by function fitting
In this subsection, we use w and p as independent variables, and define a function fitting problem as:
where w_{c} = C_{01}/C_{10}, w = w_{1}/(w_{1} + w_{2}) and p represents the proportion of positive and negative classes. We set C_{10} to 1 to reduce the complexity of calculation, i.e., f_{c} = C_{01}.
The sample distribution p, the optimal weight w_{c} = C_{01}/C_{10} and the highest fitness value of each dataset are listed in Table 6.
We use an automatic fitting software named 1STOPT to do the function fitting [47]. In 1STOPT, LevenbergMarquardt and Universal Global Optimization are used to fit functions. We compared 500 functions with different types, and selected the three functions with the highest correlation coefficient:
where a_{1} = 1.323, a_{2} = − 2.278, a_{3} = 3.047, a_{4} = − 1.286, a_{5} = − 1.746, a_{6} = 0.998, a_{7} = − 0.400, a_{8} = 0.369, a_{9} = − 2.606, a_{10} = 2.544, a_{11} = − 0.818, a_{12} = 0.482. The correlation coefficient R_{1} of f_{1} is 0.96346.
where b_{1} = 1.008, b_{2} = 2.618, b_{3} = 1.743, b_{4} = − 0.808, b_{5} = 0.297, b_{6} = 2.327, b_{7} = 4.605, b_{8} = 0.406, b_{9} = 0.699, b_{10} = − 2.343, b_{11} = − 4.984. The correlation coefficient R_{2} of f_{2} is 0.95903.
where c_{1} = 1.279, c_{2} = 0.574, c_{3} = 0.943, c_{4} = − 0.152, c_{5} = − 0.291, c_{6} = 0.113, c_{7} = 0.154, c_{8} = 0.009, c_{9} = 0.018, c_{10} = − 0.062, c_{11} = − 0.250. The correlation coefficient R_{3} of f_{3} is 0.95244.
We compare the fitting functions with the optimal weights in Figs. 1, 2 and 3.
Figures 1, 2 and 3 show the comparison results of the threedimensional interpolation of optimal weights and fitting functions. The red surface represents the optimal weights. The green, yellow, blue planes are fit surfaces of f_{1}, f_{2} and f_{3}. The correlation coefficient R of f_{1}, f_{2} and f_{3} identified that the overall fitness of the function f_{1} is better than other two. The function f_{2} gradually deviates from optimal weights while we increase the value of w, and decrease the value of p. The function f_{3} is slightly coarser than the function f_{1} in general.
Discussion
Comparison with grid searching and function fitting
Using different gene expression datasets, we compared the optimal cost weights obtained from the grid searching strategy and fitted functions f_{1}, f_{2} and f_{3}. In Table 6, we compared the WCAs with four different datasets, namely, Ovarian, Prostate, Lung1 and Lung2. The majority over minority class proportion of the four datasets are 1.68, 2.5, 5 and 8 respectively. All WCAs are computed using ELM as the base classifier. We also compare the two approaches with ECSELM. The best fit datasets are listed in Table 6.
For each dataset, we plot the weight variance with different values of w. For different dataset, the fittest function (choice from f_{1}, f_{2} and f_{3}) might be different (Fig. 4).
Figure 4 shows that the more unbalanced the dataset is, the higher degree of fitness we can get; and the cost weights obtained from the fitting functions are closer to the optimal weights. In addition, the cost weights from function f_{1} and f_{3} are slightly superior to f_{2}. We put all cost weights obtained by different methods in a threedimensional picture and show the results in Fig. 5.
For each dataset, we also illustrate the comparison of WCAs against different w values (Fig. 6). Besides, we compare WCAs of optimal weights and f_{1–3} with ECSELM [48].
In Fig. 6, we can see that the WCAs of the three fitting functions are lower than the optimal accuracy when w is less than 0.5. The reason is that the fitting degree of the cost weights in this range is lower. Moreover, it can be seen from Fig. 6 that the WCAs of the fitting functions approach to the optimal accuracy with the increment of p. Furthermore, the WCAs of our approaches is better than ECSELM in most field. Compared with ECSELM, our methods are more stable, and meanwhile can guarantee high WCA. This proves the robustness of our strategy. Similar to the case of cost weights, we ensemble all WCAs obtained by different methods in a threedimensional picture (Fig. 7). In summary, we find that the function f_{1} provides better classification performance than the other two functions in general; and the fitting function f_{3} and f_{2} have better performance while the valuable p is large (when p above 5).
Conclusions
In this paper, we have proposed two approaches to calculate the optimal cost weights for gene expression data. The two approaches include a grid searching strategy and a function fitting method. They enrich the ways of calculating the cost weights for imbalanced data classification problems. In general, the function fitting approach is more efficient than the grid searching strategy. The experimental results also show that the function fitting approach can accurate find the optimal cost weights for imbalanced gene expression datasets.
The limitation of this work is that, although the ELM classifier is tested, the stability of the function fitting method is not proven, especially for other significantly different datasets. The exploration of the proposed algorithm’s stability is left as future work.
Availability of data and materials
The datasets analyzed in this manuscript are available from http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/
Abbreviations
 ACA:

Adaptive classification accuracy
 CCR:

Correct classification rates
 CSL:

Cost sensitive learning
 OA:

Overall accuracy
 WCA:

Weighted classification accuracy
References
 1.
Golub T, Slonim D, Tamayo P, Huard C, Gaasenbeek M, Mesirov J, Coller H, Loh M, Downing J, Caligiuri M. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999;286(2):531–7.
 2.
Shipp M, Ross K, Tamayo P, Weng A, Kutok J, Aguiar R, Gaasenbeek M, Angelo M, Reich M, Pinkus G. Diffuse large bcell lymphoma outcome prediction by geneexpression profiling and supervised machine learning. Nat Med. 2002;8(1):68–74.
 3.
Veer L, Dai H, Vijver M, He Y, Hart A, Mao M, Peterse H, Kooy K, Marton M, Witteveen A. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415(6871):530–6.
 4.
Deng S, Zhu L, Huang D. Predicting hub genes associated with cervical cancer through gene coexpression networks. IEEE Comput Soc Press. 2016;13(1):27–35.
 5.
Deng S, Zhu L, Huang D. Mining the bladder cancerassociated genes by an integrated strategy for the construction and analysis of differential coexpression networks. BMC Genomics. 2015;16(Suppl 3):1–10.
 6.
Wang W, Lou B, Li X, Lou X, Jin N, Yan K. Intelligent maintenance frameworks of largescale grid using genetic algorithm and kmediods clustering methods. World Wide Web. 2019;2019(7):1573–413.
 7.
Deng S, Cao S, Huang D, Wang Y. Identifying stages of kidney renal cell carcinoma by combining gene expression and DNA methylation data. IEEE/ACM Trans Comput Biol Bioinform. 2016;14(5):1147–53.
 8.
Elkan C. The foundations of costsensitive learning. In: Seventeenth International Joint Conference on Artificial Intelligence; 2001. p. 973–8.
 9.
Zhou Z, Liu X. Training costsensitive neural networks with methods addressing the class imbalance problem. IEEE Trans Knowl Data Eng. 2006;18(1):63–77.
 10.
Yan K, Zhong C, Ji Z, Huang J: Semisupervised learning for early detection and diagnosis of various air handling unit faults 2018, 181(12):75–83.
 11.
Liu X, Zhou Z. The influence of class imbalance on costsensitive learning: An empirical study. In: International Conference on Data Mining; 2007. p. 970–4.
 12.
Maheshwari S, Jain R, Jadon R. An insight into rare class problem: analysis and potential solutions. J Comput Sci. 2018;14(8):777–92.
 13.
Hu M, Li W, Yan K, Ji Z, Hu H: Modern machine learning techniques for univariate tunnel settlement forecasting: A comparative study 2019, 2019(4):1–12.
 14.
Chai X, Deng L, Yang Q, Ling C. Testcost sensitive naive bayes classification. In: IEEE International Conference on Data Mining; 2004. p. 51–8.
 15.
Feng S. A costsensitive decision tree under the condition of multiple classes. In: International Conference on Logistics Engineering, Management and Computer Science; 2015. p. 1212–8.
 16.
Zhao H, Li X. A cost sensitive decision tree algorithm based on weighted class distribution with batch deleting attribute mechanism. Inf Sci. 2016;378(2):303–16.
 17.
Quinlan J. C4.5: Programs for machine learning: Morgan Kaufmann Publishers Inc; 1992.
 18.
Liu K, Huang D. Cancer classification using rotation forest. Comput Biol Med. 2008;38(5):601–10.
 19.
Lu H, Yang L, Yan K, Xue Y, Gao Z. A costsensitive rotation forest algorithm for gene expression data classification. Neurocomputing. 2017;228(C):270–6.
 20.
Turney P. Costsensitive classification: empirical evaluation of a hybrid genetic decision tree induction algorithm. AI Access Foundation. 1994;2(1):369–409.
 21.
Cao P, Zhao D, Zaiane O. An optimized costsensitive svm for imbalanced data learning. In: PacificAsia Conference on Knowledge Discovery and Data Mining; 2013. p. 280–92.
 22.
Yuan H, Zhang X. Multiscale fragile watermarking based on the gaussian mixture model. IEEE Trans Image Process. 2006;15(10):3189–200.
 23.
Cheng D, Wu M. A novel classifier  weighted features costsensitive svm. In: IEEE International Conference on Internet of Things; 2017. p. 598–603.
 24.
Silva J, Bacao F, Caetano M. Specific land cover class mapping by semisupervised weighted support vector machines. Remote Sens. 2017;9(2):1–16.
 25.
Cao P, Liu X, Zhao D, Zaiane O. Cost sensitive ranking support vector machine for multilabel data learning. In: International Conference on Hybrid Intelligent Systems; 2016. p. 244–55.
 26.
Zong W, Huang G, Chen Y. Weighted extreme learning machine for imbalance learning. Neurocomputing. 2013;101(3):229–42.
 27.
Zheng E, Zhang C, Liu X, Lu H, Sun J. Costsensitive extreme learning machine. In: International Conference on Advanced Data Mining and Applications; 2013. p. 478–88.
 28.
Liu Y, Lu H, Yan K, Xia H, An C. Applying costsensitive extreme learning machine and dissimilarity integration to gene expression data classification. Comput Intell Neurosci. 2016;2016(8):1–9.
 29.
Yan K, Ji Z, Lu H, Huang J, Shen W, Xue Y. Fast and accurate classification of time series data using extended elm: application in fault diagnosis of air handling units. IEEE Trans Syst Man Cybern Syst. 2017;49(7):1–8.
 30.
Zhang L, Zhang D. Evolutionary costsensitive extreme learning machine. IEEE Trans Neural Netw Learn Syst. 2017;28(12):3045–60.
 31.
Wang S, Li X, Zhang S, Gui J, Huang D. Tumor classification by combining pnn classifier ensemble with neighborhood rough set based gene reduction. Comput Biol Med. 2010;40(2):179–89.
 32.
Wang S, Zhu Y, Jia W, Huang D. Robust classification method of tumor subtype by using correlation filters. IEEE/ACM Trans Comput Biol Bioinform. 2012;9(2):580–91.
 33.
Zhu H, Wang X. A costsensitive semisupervised learning model based on uncertainty. Neurocomputing. 2017;251(8):106–14.
 34.
Ailijiang A, Charapko A, Demirbas M. Consensus in the cloud: Paxos systems demystified. In: 25th International Conference on Computer Communication and Networks (ICCCN). Waikoloa: IEEE; 2016. p. 1–10.
 35.
Zheng C, Huang D, Kong X, Zhao X. Gene expression data classification using consensus independent component analysis. Genomics Proteomics Bioinformatics. 2008;6(2):74–82.
 36.
Yan K, Ji Z, Shen W. Online fault detection methods for chillers combining extended kalman filter and recursive oneclass svm. Neurocomputing. 2017;228(3):205–12.
 37.
Wang X, Wang J, Yan K. Gait recognition based on gabor wavelets and (2d) 2 pca. Multimed Tools Appl. 2017;77(10):1–17.
 38.
Pei S, Huang D. Cooperative competition clustering for gene selection. J Clust Sci. 2006;17(4):637–51.
 39.
Zheng C, Zhang L, Ng V, Shiu S, Huang D. Molecular pattern discovery based on penalized matrix decomposition. IEEE/ACM Trans Comput Biol Bioinform. 2011;8(6):1592–603.
 40.
Zheng C, Zhang L, Ng T, Shiu C, Huang D. Metasamplebased sparse representation for tumor classification. IEEE/ACM Trans Comput Biol Bioinform. 2011;8(5):1273–82.
 41.
Zheng C. Tumor clustering using nonnegative matrix factorization with gene selection. IEEE/ACM Trans Comput Biol Bioinform. 2009;4(13):599–607.
 42.
Huang D, Zheng C. Independent component analysisbased penalized discriminant method for tumor classification using gene expression data. Bioinformatics. 2006;22(15):1855–62.
 43.
Huang D, Yu H. Normalized feature vectors: a novel alignmentfree sequence comparison method based on the numbers of adjacent amino acids. IEEE/ACM Trans Comput Biol Bioinform. 2013;10(2):457–67.
 44.
Zhao X, Dheung Y, Huang D. Analysis of gene expression data using rpem algorithm in normal mixture model with dynamic adjustment of learning rate. Int J Pattern Recognit Artif Intell. 2010;24(4):651–66.
 45.
Zheng C, Huang D, Shang L. Feature selection in independent component subspace for microarray data classification. Neurocomputing. 2006;69(16):2407–10.
 46.
Zheng C, Huang D, Sun Z, Lyu M, Lok T. Nonnegative independent component analysis based on minimizing mutual information technique. Neurocomputing. 2006;69(7):878–83.
 47.
Cheng X, Chai F, Gao J, Zhang K. 1stopt and global optimization platformcomparison and case study. In: Proceedings of the 4th IEEE International Conference on Computer Science and Information Technology; 2011. p. 18–21.
 48.
Alejo R, Sotoca JM, García V, Valdovinos RM. Costsensitive neural networks and editing techniques for imbalance problems. In: Mexican Conference on Pattern Recognition. Berlin, Heidelberg: Springer; 2010. p. 180–8.
Acknowledgments
We thank T. R. Golub, D. K. Slonim et al. providing us the gene expression data.
About this supplement
This article has been published as part of BMC Bioinformatics Volume 20 Supplement 25, 2019: Proceedings of the 2018 International Conference on Intelligent Computing (ICIC 2018) and Intelligent Computing and Biomedical Informatics (ICBI) 2018 conference: bioinformatics. The full contents of the supplement are available online at https://bmcbioinformatics.biomedcentral.com/articles/supplements/volume20supplement25.
Funding
This study is supported by National Natural Science Foundation of China (Nos. 61272315, 61402417, 61602431, 61701468, 61850410531, 61572164 and 61877015), Zhejiang Provincial Natural Science Foundation (Nos. Y1110342, LY15F020037 and LY19F020016) and International Cooperation Project of Zhejiang Provincial Science and Technology Department (No. 2017C34003).
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HL and MY conceived the project. YX developed the methodology, analyzed the results and wrote the manuscript. KY, ZG and QJ provided administrative and technical support. All authors read and approved the final manuscript.
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Correspondence to Minchao Ye.
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Lu, H., Xu, Y., Ye, M. et al. Learning misclassification costs for imbalanced classification on gene expression data. BMC Bioinformatics 20, 681 (2019). https://doi.org/10.1186/s128590193255x
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Keywords
 Costsensitive
 Misclassification cost
 Weighted classification accuracy
 Parameter fitting