Algorithm 1: ILECA

Input: train set $D=\left\{x_k,t_k\right\},k=1,2,\dots ,N$, test set $DT=\left\{T{x}_k,T{t}_k\right\},k=1,2,\dots ,n_t$ Output: expected classification matrix T  1:formulate the feature matrix X for D  2:$X=Z-score\left(X\right)$  3:calculate $S_b,S_w,S_b-S_w$  4:obtain $A^{*}$ by solving the eigenproblem of $I^{-1}(S_b-S_w)$  5:calculate $Y=X{A}^{*}$, obtain the new train data $D^{{}^{\prime }}=\left\{y_k,t_k\right\}$  6:generate $w_i$ and $b_i$ randomly, set the number of hidden neurons L  7:calculate the output of hidden neurons H according to the Equation (13)  8:calculate the output weight of classifier $\beta$ according to the Equation (14)  9:formulate the feature matrix $X_t$ for $DT$ 10:$X_t=Z-score\left(X_t\right)$ 11:$Y_t=X_t{A}^{*}$ 12:calculate the output of hidden neurons $H_t$ for test data according to the Equation (13) 13:$T=H_t\beta$ according to the Equation (12) 14:returnT