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Table 4 Improvement in classification accuracy using majority voting ensembles. Optimal unweighted majority-voting ensemble classifiers were formed by selecting classifiers from all 8 classifiers for each feature set listed and the average classification accuracy for 10-fold cross-validation was calculated. A paired-t test was performed for each ensemble classifier against the previous neural network classifier for each feature subset (SLF15 and SLF16 were compared against the previous classifier for SLF8 and SLF13, respectively). Each ensemble classifier was also compared against the optimal classifier for each feature set listed in Table 2 (SLF15 and SLF16 were compared with the individual optimal classifiers for SLF8 and SLF13, respectively).

From: Boosting accuracy of automated classification of fluorescence microscope images for location proteomics

Feature Set

Classifiers in the Optimal Majority-voting Ensemble

Average classification accuracy (%)

P-value of paired t test with previous results

P-value of paired t test with optimal single classifier

Classification Accuracy Upper Bound* (%)

SLF8 (2D)

Exprbf-kernel SVM

AdaBoost

Bagging

89.4

0.003

0.08

95.5

SLF15 (2D)

Rbf-kernel SVM

Exponential-rbf-kernel SVM

Polynomial-kernel SVM

91.5

0.0006

0.01

96.1

SLF13 (2D DNA)

Rbf-kernel SVM

AdaBoost

Mixtures-of-Experts

90.7

0.003

0.03

95.6

SLF16 (2D DNA)

Neural Network

Linear-kernel SVM

Exprbf-kernel SVM

Polynomial-kernel SVM

AdaBoost

92.3

0.003

0.02

96.6

SLF14 (3D)

Neural Network

Linear-kernel SVM

Exprbf-kernel SVM

Polynomial-kernel SVM

AdaBoost

89.8

0.02

0.29

96.3

SLF10 (3D DNA)

Linear-kernel SVM

Rbf-kernel SVM

Exprbf-kernel SVM

Mixtures-of-Experts

95.8

0.02

0.35

98.2

  1. * The upper bound of classification accuracy for a feature set is defined as the percentage of all images that could be correctly classified by at least one of the eight tested classifiers using that feature set.