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Table 3 Accuracy of a dozen of different combinations of feature selection and learning methods

From: Breast cancer prediction using genome wide single nucleotide polymorphism data

 

Feature Selection Methods

 

Information Gain

MeanDiff

mRMR

PCA

Learning Methods

Decision Tree

50.88%

52.06%

51.20%

51.69%

 

KNN

56.17%

58.71%

57.78%

51.36%

 

SVM-RBF

55.37%

57.30%

56.18%

51.84%

  1. 10-fold cross validation accuracies of combination of 4 feature selection methods and 3 learning methods shows that none of these combinations are more accurate than our suggested combination of MeanDiff500 feature selection and BestKNN learning (59.55%); indeed, several do not even beat the baseline of 51.52%.
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BMC Bioinformatics

ISSN: 1471-2105

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