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. 2019 Aug 1;2019:7362931. doi: 10.1155/2019/7362931

Table 3.

Performance of the proposed Mr2DNM for five datasets.

Dataset NF # Reduction (%) Optimal feature sequence Accuracy (%) Time (×103 s) AUC
WBCD 9 7 22.22 F2, F6, F1, F7, F5, F3, F8 96.80 54.4 0.9942
BUPA 7 5 28.57 F5, F6, F1, F4, F3 72.66 7.1 0.7458
IONO 34 8 76.47 F5, F1, F8, F4, F3, F28, F7, F14 90.73 24.6 0.9227
PIMA 8 7 12.5 F2, F5, F8, F6, F4, F1, F3 76.80 33.2 0.8198
VOTE 16 6 62.5 F4, F5, F12, F3, F14, F8 96.57 10.2 0.9779