面向不平衡数据分类的KFDA-Boosting算法

he imbalance of data distribution and the nonlinearity of data characteristics increase the difficulty of classification, especially the recognition of the minority class samples in the imbalanced data, thus affecting the overall classification effect. For the above problem, an algorithm called KFDA-Boosting was proposed in this paper, which combined the characteristic of KFDA , namely Kernel Fisher Discriminant Analysis, effectively extracting the samples nonlinear features and the idea of Boosting algorithm in the ensemble learning. In order to verify the effectiveness and superiority of the algorithm in the classification of imbalanced data, the paper used the G-mean value, the precision and recall of the minority class samples to evaluate the performance of classifier, and selected 10 datasets of UCI to test the KFDA-Boosting algorithm, which compared with other six algorithms, such as support vector machine. Compared with other algorithms, the results show that the algorithm can effectively identify the minority class, and has a significant effect on the classification of imbalanced data and better stability on the whole, especially for the data with larger unbalance degree or nonlinear characteristics.