Research on Boosting-based Imbalanced Data Classification
Wang Zhi-quan · 2011
This paper aimed to investigate boosting-based imbalanced data classification algorithms.Through the deep analysis of existing algorithms,a weight-sampling boosting algorithm was proposed.Changing the data distribution by weight sampling,the trained classifier was made suitable for imbalanced data classification.The natural of the proposed algorithm is that the loss function of nave boosting is adjusted by the sampling function and the positive examples are emphasized so that the classifier focuses on correctly classifying these examples and finally the recognition rate of positive examples is improved.The new algorithm is simple and practical and has been shown to outperform naive boosting and previous algorithms in the problem of imbalanced data classification on the UCI data sets.