Algorithm 2.

Dropfeature for Training DNNs

Input: The input feature subset, $\mathcal{R}$; the feature index set, ϵ; the learning algorithm, $\mathcal{A}$.
Output: The updated feature subset and index set, ${\mathcal{R}}^{\prime }$ and ϵ′.
1:	begin
2:	Run $\mathcal{A}$ on $\mathcal{R}$, and compute AUC A by $\mathcal{A}$;
3:	Initialize the current state S = 0;
4:	repeat
5:	Use random walking to update the following three states with equal probabilities;
6:	if state S = 1 then
7:	Remove $g\subseteq \mathcal{R}$ and add $g^{\prime }\subseteq \mathcal{G}/\mathcal{R}$ to get ${\mathcal{R}}_1$ and ϵ1;
8:	Run $\mathcal{A}$ on ${\mathcal{R}}_1$ and compute AUC A1 by $\mathcal{A}$;
9:	else if state S = 2 then
10:	Remove $g\subseteq \mathcal{R}$ to get ${\mathcal{R}}_2$ and ϵ2;
11:	Run $\mathcal{A}$ on ${\mathcal{R}}_2$ and compute AUC A2 by $\mathcal{A}$;
12:	else if state S = 3 then
13:	Add $g^{\prime }\subseteq \mathcal{G}/\mathcal{R}$ to get ${\mathcal{R}}_3$ and ϵ3;
14:	Run $\mathcal{A}$ on ${\mathcal{R}}_3$ and compute AUC A3 by $\mathcal{A}$;
15:	end if
16:	if (Ai = max {A, A1, A2, A3}) then
17:	Update ${\mathcal{R}}^{\prime }={\mathcal{R}}_i$, ϵ′ = ϵi, A = Ai and search from ${\mathcal{R}}^{\prime }$;
18:	else if (A = max {A, A1, A2, A3}, and Ai = max {A1, A2, A3}) then
19:	Search from $\mathcal{R}$ with probability ui and update ${\mathcal{R}}^{\prime }=\mathcal{R}$, ϵ′ = ϵ, and A = A;
20:	or
21:	Search from ${\mathcal{R}}_i$ with probability 1 − ui and update ${\mathcal{R}}^{\prime }={\mathcal{R}}_i$, ϵ′ = ϵi, and A = Ai;
22:	end if
23:	Reinitialize the current state S = 0;
24:	until no significant improvement is achieved in AUC score.
25:	return ${\mathcal{R}}^{\prime }$ and ϵ′.
26:	end