Algorithm 1 Algorithm to apply active learning for ASD screening

Input: N = Total number of samples in D2 T2 = Test set for evaluation of matrices l = Number of labeled samples on first iteration U = N − l samples, pool of unlabeled data m = Number of iterations n = $\frac{N-l}{m}$, Number of labeled samples added per iteration Start for iteration in range (m) do   n_labeled $\leftarrow$ l + m × n   model_train (n_labeled)   f12 $\leftarrow$ feature learning (model)   w12 $\leftarrow$ model_get_weights ()   prediction $\leftarrow$ model_predict (U)   confidence $\leftarrow$ assign_confidence (prediction)   uncertain samples $\leftarrow$ query_on (prediction)   M12 $\leftarrow$ model_evaluate(T2) End