Imbalanced data learning based on particle swarm optimization
CAO Peng1,2*, LI Bo1,2, LI Wei1,2, ZHAO Dazhe1,2
1.College of Information Science and Engineering, Northeastern University, Shenyang Liaoning 110004, China;
2.Key Laboratory of Medical Image Computing of Ministry of Education (Northeastern University), Shenyang Liaoning 110179, China
Abstract：In order to improve the classification performance on the imbalanced data, a new Particle Swarm Optimization (PSO) based method was introduced. It optimized the re-sampling rate and selected the feature set simultaneously, with the imbalanced data evaluation metric as objective function through particle swarm optimization, so as to achieve the best data distribution. The proposed method was tested on a large number of UCI datasets and compared with the state-of-the-art methods. The experimental results show that the proposed method has substantial advantages over other methods; moreover, it proves that it can effectively improve the performance on the imbalanced data by optimizing the re-sampling rate and feature set simultaneously.
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