Active instance selection via parametric equation and instance overlap aware scheme

Abstract

Active learning (AL) reduces the human labeling effort by learning a classifier with the small labeled data. This small labeled data is formed by those instances, which reduce generalization error the most. As AL reduces the labeling cost, but most of the pool based query strategies evaluate all unlabeled instances in each iteration of query instance selection, which makes them computationally extensive. Also, many times the query strategies lead to the selection of redundant/overlapped instances, which leads to no improvement in the generalization performance. In this work, an advanced query strategy under pool based scenario has been proposed. This query strategy utilizes parametric equation based query synthesis as the informative criterion and the instance overlap aware scheme as the representative criterion. This informative criterion identifies the input instances near the decision boundary, which speeds up the instance selection process and hence reduces the response time. On the set of identified instances, the representative criterion is used to avoid the overlapped instance selection, which improves the generalization performance. Also, a meta-learning based approach has been used to identify the values of the main parameters used in the formulated criteria. The comparison of the proposed approach with the existing baseline solutions on artificial as well as real-world datasets demonstrates that the proposed approach achieves a significant reduction in the query instance selection time, along with an enhancement in the generalization performance over the existing approaches.