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Table 6 Comparison of the classification accuracy using the original Chi-squared and the proposed method

From: Improving feature selection performance using pairwise pre-evaluation

Chi Squared GDS1027 GDS2545 GDS2546 GDS2547 GDS3715
Orig Modi Orig Modi Orig Modi Orig Modi Orig Modi
5 KNN 0.51 0.68 0.47 0.54 0.44 0.59 0.51 0.55 0.61 0.73
SVM 0.61 0.69 0.60 0.67 0.51 0.69 0.58 0.65 0.72 0.82
10 KNN 0.60 0.64 0.49 0.57 0.50 0.57 0.51 0.58 0.70 0.78
SVM 0.65 0.74 0.61 0.67 0.57 0.66 0.62 0.70 0.69 0.87
15 KNN 0.64 0.66 0.47 0.53 0.55 0.63 0.49 0.60 0.67 0.75
SVM 0.69 0.73 0.60 0.67 0.65 0.66 0.60 0.70 0.73 0.85
20 KNN 0.64 0.70 0.51 0.53 0.54 0.64 0.56 0.64 0.67 0.83
SVM 0.73 0.77 0.60 0.70 0.63 0.69 0.65 0.74 0.72 0.86
25 KNN 0.69 0.73 0.53 0.52 0.57 0.61 0.59 0.66 0.67 0.86
SVM 0.73 0.77 0.63 0.68 0.63 0.70 0.66 0.77 0.70 0.85
30 KNN 0.62 0.71 0.55 0.53 0.54 0.60 0.60 0.65 0.71 0.85
SVM 0.75 0.77 0.61 0.68 0.63 0.70 0.66 0.74 0.74 0.87
MAX KNN 0.69 0.73 0.55 0.57 0.57 0.64 0.60 0.66 0.71 0.86
MAX SVM 0.75 0.77 0.63 0.70 0.65 0.70 0.66 0.77 0.74 0.87
  1. Orig Original algorithm, Modi Proposed modified algorithm
  2. Values in the first column are presented as the number of features selected for the classification test and the others are presented as classification accuracy. The bold numbers denote the highest value of KNN and SVM of each column