In this study, we have used various classifiers implemented in WEKA package and SVM light [ 25, 26].
FPF hence, enables the comparison of performance of various classifiers employed in the study.
Table 1 Groupwise average accuracies of various feature extraction methods combined with various classifiers applied to classify sets A, D, and E (standard deviations are noted in parentheses).
Table 2 Groupwise average accuracies of various feature extraction methods combined with various classifiers applied to classify sets A, B, C, D, and E (standard deviations are noted in parentheses).
These are the same datasets that were used in [6] for comparing TSP and k-TSP with various classifiers.
Figure 3 Variations in classification accuracies versus number of principle components with respect to various classifiers for epilepsy diagnosis.
The application of various classifiers described here has revealed that dynamic stimuli evoke informative patterns at particular moments in time.
By combining prediction results from various classifiers including alignment-based and alignment-free classifiers as well as transmembrane prediction methods in a multi-level filtering process, prioritized sets of 7TMR candidates can be obtained for further investigation.
In recent years, the application of boosting algorithm has become popular among various classifiers.
Figure 1 Achieved accuracy for the various classifiers.
