The higher the resolution, the better the classification performance, but the higher the processing time.
For the purpose of comparison, we converted the classification performance from the classification accuracy θ test into the error rate.
The experiment evaluated the classification performance of the SVM learned by the proposed features.
Through the classification performance, the stability performance, and the enrichment analysis, iRDA is a promising filter to find predictive, stable, and enriched gene-expression candidate genes.
The results of the classification performance on the selected feature subset are reported in Table 4.
The evaluation of the classification performance against the SC_GOLD corpus for each TC subtype is listed in Table 1.
In order to evaluate the ability of the features quantitatively, we utilize the classification performance as the criterion.
Experimental results show that the proposed algorithm improves the classification performance in monotone classification tasks.
And the factor to affect the classification performance is the parameters used in SVM.
These reduced dimensionality data improve the classification performance and reduce the classification time.
