Table 3. Hypothesis testing over accuracy with different combinations of methods.

Comparing Methods		Mean %	p-Value
Method1	Method2
wHLFS+RF	SVM	9.81(0.7)	<0.0001
	RF	5.04(0.56)	<0.0001
	SVM-Kernel	1.94(0.70)	0.0031
	LR	3.59(0.75)	<0.0001
	Lasso	1.42(0.59)	0.009
	Lasso+SVM	3.43(0.78)	<0.0001
	Lasso+RF	3.63(0.77)	<0.0001
	Interactions+SVM	12.4(0.99)	<0.0001
	Interactions+RF	8.06(0.66)	<0.0001
	All-Pair Lasso	2.35(0.78)	0.0015
	All-Pair Lasso+SVM	6.33(0.85)	<0.0001
	All-Pair Lasso+RF	4.77(0.88)	<0.0001
	wHLFS	2.64(0.59)	<0.0001
	wHLFS+SVM	0.94(0.57)	0.0506

MCI converter/non-converter classification comparison of different combinations of feature selection methods and classification methods in terms of accuracy. With the same input training and testing samples and the same parameters, we compare the performances based on different combinations of methods. By varying the sets of training samples and testing samples and the settings of parameters, we obtain a series of comparisons between wHLFS+RF and another combination of methods. A positive mean value means the average improvement on accuracy by using wHLFS+RF. A p-value less than 0.05 means wHLFS+RF achieves a significant improvement on accuracy. The standard deviations of mean values are shown in the parentheses.