Active Learning with Maximum Density and Minimum Redundancy

Active Learning is a machine learning technique that selects the most informative examples for labeling so that the classification performance would be improved to its maximum possibility. In this paper, a novel active learning approach based on Maximum Density and Minimum Redundancy (MDMR) is proposed. The objective of MDMR is to select a set of examples that have large density and small redundancy with others. Firstly, we propose new methods to measure the density and redundancy of examples. Then a model is built to select examples by combining density and redundancy and dynamic programming algorithm is applied to solve the problem. The results of the experiment on terrain classification have demonstrated the effectiveness of the proposed approach.