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. 2019 Sep 18;7:224. doi: 10.3389/fbioe.2019.00224

Figure 2.

Figure 2

Performance comparison of the AOPs-SVM and other classifiers. (A) Compares other SVM models generated on the original feature set (473D). SVM-473D and SVM-473D-weight are the classifiers that the SVM trained on the original feature set in straight and weighted manner (negative: positive = 1: 6). (B) Comparing with three other traditional classifiers on optimal feature set (176D). RandomForest-176D, BayesNet-176D, and AdaBoostM1-176D are RandomForest, BayesNet and AdoBoostM1 on optimal feature set, respectively. (C) Comparing with other SVM models based on optimal feature set generated by ANOVA and mRMR respectively. ANOVA, mRMR generated 302D and 180D optimal feature set, respectively. (D) Comparing with state-of-the art methods. “ < ” denotes that Sn and SP of SeqSVM are <0.65 and 0.935, respectively.