Heuristic improvement for active learning using localized generalization error as selection criterion

Owing to the growth of Internet and computer technology, pattern recognition for large-scale datasets has become one of the hot research topics. The major challenges are to reduce the human efforts involved and to improve the efficiency. Traditional passive learning methods require labeling of all training samples may not be feasible in large-scale recognition problems because of the requirement of large-scale class labeling for the huge number of training samples. In the literatures, there are many studies on active learning methods, which does not require all training samples to be labeled and it selects training samples for labeling based on the knowledge of the current classifier. In this paper, we present an active learning method using localized generalization error of candidate sample as selection criterion. Our method uses the generalization error of candidate sample, so theoretically it should have a better performance than other methods. From the experiment results, our method outperforms other methods in both yielding higher prediction accuracy on testing dataset and selecting fewer training samples. Furthermore, we propose a heuristics improvement based on the Q-neighborhood idea of the localized generalization error model to reduce the number of samples being selected and the computational time.