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Table 3 The selected features and the obtained classification accuracies by the use of the PCA-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 coefficient of MFCCs 89.9
Energy at the eight, 18th, 19th, 32nd, 33th, and 35th nodes of WP tree
Entropy at the first, fourth, eight, 16th, 17th, 32nd, 35th, and 39th nodes of WP tree
(Feature vector length = 15)
‘trainscg’ The first and second coefficients of MFCCs 90.7
Energy at the eight, 18th, 19th, 32nd, 33th, and 35th nodes of WP tree
Entropy at the first, fourth, eight, 16th, 17th, 32nd, 35th, and 39th nodes of WP tree
(Feature vector length = 16)
‘traingdx’ The first, second, fourth, and fifth coefficients of MFCCs 89.9
Energy at the eight, 18th, 19th, 32nd, 33th, and 35th nodes of WP tree
Entropy at the first, fourth, eight, 16th, 17th, 32nd, 35th, and 39th nodes of WP tree
(Feature vector length = 18)