An effective heuristic for developing hybrid feature selection in high dimensional and low sample size datasets

BMC Bioinformatics

Table 2 List of benchmark datasets

No	Dataset	Disease	Instances	Features	Dimensionality Index*	Balance Ratio**
1	ALLAML	Leukemia	72	7129	2.07	0.347
2	alon	Colon Cancer	62	2000	1.84	0.355
3	borovecki	Huntington	31	22,283	2.92	0.452
4	chiaretti	Leukemia	128	12,625	1.95	0.422
5	chin	Breast Cancer	118	22,215	2.1	0.364
6	chowdary	Breast Cancer	104	22,283	2.16	0.404
7	GLI_85	Gliomas	85	22,283	2.25	0.306
8	gordon	Lung Cancer	181	12,533	1.82	0.171
9	gravier	Breast Cancer	168	2905	1.56	0.339
10	pomeroy	CNS Tumor	60	7128	2.17	0.35
11	Prostate_GE	Prostate Cancer	102	5966	1.88	0.49
12	shipp	Lymphoma	77	7129	2.04	0.247
13	singh	Prostate Cancer	102	12,600	2.04	0.49
14	SMK_CAN_187	Lung cancer	187	19,993	1.89	0.481
15	subramanian	N/A	50	10,100	2.36	0.34
16	tian	Myeloma	173	12,625	1.83	0.208
17	west	Breast Cancer	49	7129	2.28	0.49
18	arcene		200	10,000	1.74	0.44
19	gisette		7000	5000	0.96	0.5
20	Hill_valley		1212	100	0.65	0.495
21	ionosphere		351	33	0.6	0.359
22	madelon		2600	500	0.79	0.5
23	sonar		208	60	0.77	0.466
24	wdbc		569	30	0.54	0.373

^* Dimensionality index = log(\(Number of features\))/log(\(Number of instances\)). It is a measure of how high-dimensional a given dataset is
^**Balanced ratio: the proportion of the samples in lower class of the entire dataset. A value 0.5 is ideal for a binary class dataset

ISSN: 1471-2105