Tri-partition cost-sensitive active learning through kNN

Active learning differs from the training-testing scenario in that class labels can be obtained upon request. It is widely employed in applications where the labeling of instances incurs a heavy manual cost. In this paper, we propose a new algorithm called tri-partition active learning through k-nearest neighbors (TALK). The optimization objective is to minimize the total teacher and misclassification costs. First, a k-nearest neighbors classifier is employed to divide unlabeled instances into three disjoint regions. Region I contains instances for which the expected misclassification cost is lower than the teacher cost, Region II contains instances to be labeled by human experts, and Region III contains the remaining instances. Various strategies are designed to determine which instances are in Region II. Second, instances in Regions I and II are labeled and added to the training set, and the tri-partition process is repeated until all instances have been labeled. Experiments are undertaken on eight University of California, Irvine, datasets using different cost settings. Compared with the state-of-the-art cost-sensitive classification and active learning algorithms, our new algorithm generally exhibits a lower total cost.