Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes

Table 5 Performance of the cost sensitive classifier algorithms on the Entrez gene dataset

Classifier algorithms	TP rate/Recall	FP rate	Accuracy	Precision	F-measure	MCC
Bayes Net	0.662	0.214	0.782	0.076	0.136	0.169
Decision Table	0.268	0.243	0.744	0.028	0.051	0.009
DTNB	0.296	0.205	0.781	0.037	0.065	0.035
Functional Tree	0.648	0.253	0.744	0.063	0.115	0.141
J48	0.620	0.211	0.784	0.072	0.129	0.155
Logistic Regression	0.690	0.20	0.797	0.084	0.149	0.190
LWL (J48 + KNN)	0.676	0.213	0.783	0.077	0.139	0.175
Naive Bayes	0.718	0.199	0.799	0.087	0.156	0.201
NB Tree	0.493	0.229	0.764	0.054	0.097	0.098
Random Forest	0.592	0.196	0.798	0.074	0.131	0.154
SVM	0.788	0.233	0.767	0.082	0.148	0.203