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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%.