  We study a logistic model-based active learning procedure for binary
classification problems, in which we adopt a batch subject selection strategy
with a modified sequential experimental design method. Moreover, accompanying
the proposed subject selection scheme, we simultaneously conduct a greedy
variable selection procedure such that we can update the classification model
with all labeled training subjects. The proposed algorithm repeatedly performs
both subject and variable selection steps until a prefixed stopping criterion
is reached. Our numerical results show that the proposed procedure has
competitive performance, with smaller training size and a more compact model,
comparing with that of the classifier trained with all variables and a full
data set. We also apply the proposed procedure to a well-known wave data set
(Breiman et al., 1984) to confirm the performance of our method.
