A new approach to enhance the performance of decision tree for classifying gene expression data

BMC Proceedings

Table 6 Performance in AUC.

Method	GE1	GE2	GE3	GE4	GE5	GE6	GE7
BVROC-Tree	0.69 ± 0.04	0.82 ± 0.04	0.93 ± 0.03	0.49 ± 0.22	0.89 ± 0.04	0.97 ± 0.01	0.77 ± 0.06
ROC-Tree	0.64 ± 0.09	0.79 ± 0.05	0.93 ± 0.04	0.29 ± 0.05	0.89 ± 0.33	0.95 ± 0.01	0.54 ± 0.08
AUCsplit	0.57 ± 0.10	0.78 ± 0.02	0.92 ± 0.02	0.30 ± 0.06	0.81 ± 0.04	0.82 ± 0.08	0.49 ± 0.11
C4.5	0.56 ± 0.05	0.78 ± 0.03	0.87 ± 0.03	0.39 ± 0.04	0.78 ± 0.06	0.83 ± 0.02	0.45± 0.05
ADTree	0.57 ± 0.04	0.96± 0.02	0.92 ± 0.06	0.36 ± 0.05	0.84 ± 0.03	0.90 ± 0.08	0.50 ± 0.06
REPTree	0.59 ± 0.06	0.80 ± 0.02	0.91 ± 0.05	0.40 ± 0.07	0.79 ± 0.04	0.88 ± 0.07	0.61± 0.08
Random Tree	0.55 ± 0.03	0.64 ± 0.04	0.85 ± 0.12	0.43 ± 0.09	0.63 ± 0.05	0.81 ± 0.14	0.53 ± 0.15
Random Forest	0.54 ± 0.05	0.89 ± 0.04	0.88 ± 0.12	0.43 ± 0.09	0.79 ± 0.03	0.83 ± 0.13	0.47 ± 0.21
Naïve Bayes	0.55 ± 0.05	0.93± 0.02	0.89 ± 0.12	0.42 ± 0.09	0.53 ± 0.05	0.86 ± 0.14	0.65 ± 0.11
k-NN	0.53 ± 0.03	0.93 ± 0.02	0.91 ± 0.11	0.42 ± 0.09	0.79 ± 0.05	0.87 ± 0.13	0.51 ± 0.09

Table representing the AUC result for gene expression datasets using 5 × 10 fold cross-validation scheme

ISSN: 1753-6561