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Table 5 Comparison of different classifiers when 5% of the class samples are used as gallery and remaining 95% of sample are used as test on 38 snake feature database

From: Discriminative histogram taxonomy features for snake species identification

Method % Correct F-score AUC Precision (%) Recall (%)
Bayes net [37] 78.81 ± 2.27 0.98 ± 0.01 0.89 ± 0.03 83 ± 0.07 97 ± 0.03
Naïve Bayes [27] 77.69 ± 2.11 0.97 ± 0.01 0.89 ± 0.02 90 ± 0.05 89 ± 0.03
Multilayer perceptron [26] 86.85 ± 2.59 0.98 ± 0.01 0.92 ± 0.02 90 ± 0.04 94 ± 0.04
Ada BoostM1 [28] 57.39 ± 1.44 0.80 ± 0.03 0.75 ± 0.02 62 ± 0.04 95 ± 0.05
Multi BoostAB [29] 57.39 ± 1.44 0.80 ± 0.03 0.75 ± 0.02 62 ± 0.04 95 ± 0.05
RBF network [30] 85.00 ± 3.05 0.95 ± 0.02 0.91 ± 0.03 92 ± 0.05 89 ± 0.04
IB1 [31] 85.82 ± 2.45 0.93 ± 0.02 0.91 ± 0.02 88 ± 0.04 93 ± 0.04
IBk [31] 86.38 ± 2.47 0.94 ± 0.01 0.91 ± 0.02 88 ± 0.04 94 ± 0.03
LWL [32] 68.37 ± 6.26 0.97 ± 0.01 0.82 ± 0.05 72 ± 0.07 96 ± 0.03
NB Tree [36] 83.79 ± 2.87 0.95 ± 0.02 0.91 ± 0.03 88 ± 0.05 95 ± 0.04
J48 [33] 78.92 ± 4.37 0.98 ± 0.01 0.87 ± 0.04 80 ± 0.06 97 ± 0.03
Random sub space [34] 80.50 ± 3.29 0.97 ± 0.02 0.90 ± 0.03 85 ± 0.06 96 ± 0.04
Bagging [35] 80.91 ± 4.48 0.94 ± 0.03 0.88 ± 0.04 83 ± 0.06 93 ± 0.04