Imbalanced data classification algorithm based on hybrid model

摘要

This paper proposes a method to deal with imbalanced data in classification. Particle of Swarm Optimization (PSO) algorithm is used to optimize the SVM parameters, and the optimized SVM is used as weak classifier for AdaBoost inside cascade model. The experimental results show that the method significantly improves the overall classification accuracy and the recognition rate of the rare class.