Cost-sensitive active learning with a label uniform distribution model

Abstract

Active learning is a man-machine interaction scenario in which the machine acquires information actively from the expert. Cost-sensitive active learning balances the misclassification cost with the teacher cost paid for label queries. Inspired by granular computing (GrC) and three-way decision (3WD), this paper presents a new algorithm called cost-sensitive active learning through density clustering under a label uniform distribution model (CADU). CADU iteratively divides the universe, queries labels, and classifies instances until each label is queried or predicted. The density clustering technique is used to divide the universe into blocks. A label uniform distribution model is built to calculate the expected label distribution of each block. According to the teacher and misclassification cost settings, an optimization function is designed to determine the number of labels to be queried. Comparison study with 10 state-of-the-art algorithms are undertaken on 12 public datasets. Results show that CADU outperforms others in terms of average cost.