TABLE II.

Performance of Different Feature Selection Methods on Three Public Data Sets

		LSVT Voice	Colon Cancer	Leukemia Cancer
Instances		126	62	72
Features		309	2,000	7,129
SVM-FoBa	No. features	47	29–30	30–38
	Accuracy	90.48%	87.10%	98.61%
	Sensitivity	85.71%	92.50%	96.00%
	Specificity	92.86%	86.36%	100%
Forward selection	No. features	83	37–39	39–43
	Accuracy	87.30%	85.48%	97.22%
	Sensitivity	85.71%	90.00%	96.00%
	Specificity	90.48%	77.27%	100%
SVM-RFE	No. features	67–68	31	82–84
	Accuracy	84.92%	87.10%	98.61%
	Sensitivity	85.71%	90.00%	96.00%
	Specificity	86.90%	86.36%	100%
T-test ranking*	No. features	47	29–30	30–38
	Accuracy	88.89%	87.10%	95.83%
	Sensitivity	83.33%	87.50%	96.00%
	Specificity	91.67%	86.36%	95.74%
Fisher score ranking*	No. features	47	29–30	30–38
	Accuracy	88.89%	87.10%	95.83%
	Sensitivity	80.95%	87.50%	96.00%
	Specificity	92.86%	86.36%	95.74%

^*T-Test and Fisher score method used the same number of features with that of SVM-FoBa for equivalent comparisons.

“LSVT Voice Rehabilitation” data set was obtained from the UCI Machine Learning Repository (http://archive.ics.uci.edu/ml/datasets.html).

Descriptions of “Colon Cancer” and “Leukemia Cancer” data can be found at http://www.inf.ed.ac.uk/teaching/courses/dme/html/datasets0405.html.