Table 3.

Random forest classification results (the definition of the features can be found in Table A1 and Table A2 in the Appendix A).

Classification Method	Feature Selection Algorithm	Accu.	Sensi.	Speci.	Preci.	Best Features Sub-Set
Random Forest	SFFS	0.8889	0.8947	0.8824	0.894	$$\begin{gathered} 8\mathrm{attributes} \\ \{f_2, f_9, f_{16}, f_{25}, f_{27}, f_{28}, f_{34}, f_{38}\} \end{gathered}$$
	SBFS	0.9167	0.9444	0.888	0.894	$$\begin{gathered} 10\mathrm{attributes} \\ \{f_1, f_2, f_{14}, f_{18}, f_{21}, f_{24}, f_{27}, f_{28}, f_{32}, f_{35}\} \end{gathered}$$