Skip to main content

Table 4 Comparison of different classifiers when 5% of the class samples are used as gallery and remaining 95% of sample are used as test on top 15 selected snake feature database

From: Discriminative histogram taxonomy features for snake species identification

Method % Correct F-score AUC Precision (%) Recall (%)
Bayes net [37] 81.26 ± 4.00 0.98 ± 0.01 0.92 ± 0.03 88 ± 0.05 96 ± 0.04
Naïve Bayes [27] 81.64 ± 3.05 0.98 ± 0.01 0.93 ± 0.03 91 ± 0.04 96 ± 0.04
Multilayer perceptron [26] 86.64 ± 2.71 0.97 ± 0.01 0.92 ± 0.02 90 ± 0.04 95 ± 0.03
Ada BoostM1 [28] 57.52 ± 1.27 0.80 ± 0.03 0.75 ± 0.02 63 ± 0.04 95 ± 0.04
Multi BoostAB [29] 57.52 ± 1.27 0.80 ± 0.03 0.75 ± 0.02 63 ± 0.04 95 ± 0.04
RBF network [30] 88.75 ± 2.69 0.97 ± 0.02 0.94 ± 0.02 93 ± 0.04 96 ± 0.03
IB1 [31] 86.05 ± 3.35 0.93 ± 0.03 0.91 ± 0.04 89 ± 0.04 94 ± 0.07
IBk [31] 87.50 ± 2.35 0.95 ± 0.01 0.92 ± 0.02 88 ± 0.04 96 ± 0.03
LWL [32] 69.57 ± 4.17 0.97 ± 0.01 0.86 ± 0.04 77 ± 0.06 96 ± 0.03
J48 [33] 84.71 ± 2.90 0.95 ± 0.02 0.91 ± 0.03 87 ± 0.06 96 ± 0.03
Random sub space [34] 79.77 ± 4.01 0.98 ± 0.01 0.89 ± 0.03 82 ± 0.06 98 ± 0.02
Bagging [35] 81.34 ± 3.93 0.97 ± 0.02 0.90 ± 0.03 85 ± 0.06 96 ± 0.03
NB Tree [36] 82.10 ± 4.02 0.96 ± 0.02 0.91 ± 0.03 87 ± 0.05 96 ± 0.03