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Table 4 The selected features and the obtained classification accuracies by the use of the GA-based method

From: A novel hybrid of genetic algorithm and ANN for developing a high efficient method for vocal fold pathology diagnosis

Training algorithm Selected features Classification accuracy (%)
‘trainrp’ The first, fifth, and eight coefficients of MFCCs 89.9
Energy at the seventh, ninth, 36th, 40th, 42nd, 48th, 49th, 51st, 61st, and 62nd nodes of WP tree
Entropy at the first, seventh, eight, 16th, 27th, 28th, 29th, 37th, 45th, 46th, 53rd, and 61st nodes of WP tree
(Feature vector length = 25)
‘trainscg’ The first and fifth coefficients of MFCCs 95.3
Energy at the 11th, 17th, 22nd, 25th, 29th, 35th, 40th, 41st, 45th, 46th, 49th, 55th, 61st, and 63rd nodes of WP tree
Entropy at the first, 13th, 15th, 19th, 26th, 35th, 36th, 42nd, 43rd, 44th, 46th, 52nd, 53rd, and 55th nodes of WP tree
(Feature vector length = 30)
‘traingdx’ The first, third, and fifth coefficients of MFCCs 93.8
Energy at the eight, 20th, 21st, 43rd, 60th, and 62nd node of WP tree
Entropy at the 11th, 13th, 35th, 52nd, 54th, 58th, 59th, and 62nd nodes of WP tree
(Feature vector length = 17)