Cost-Sensitive Active Learning for Incomplete Data

Abstract

Practical data often suffer from missing attribute values and lack of class labels. A reasonable machine learning scenario involves obtaining certain values and labels at cost on request. In this article, we propose the cost-sensitive active learning through unified evaluation and dynamic selection (CALS) algorithm to handle the learning task in this new scenario. For data representation, we consider misclassification cost, label query cost, and attribute query cost. For the cost/benefit estimation, we design a unified assessment of attribute values and labels with softmax regression. For the selection of attribute value and label, we propose an optimal acquisition scheme with permutation and greedy strategies. We perform experiments with synthetic, benchmark, and domain datasets. The results of the significance test verify the effectiveness of CALS and its superiority over cost-sensitive active learning and missing data imputation algorithms.