A New Improved Boosting for Imbalanced Data Classification
Zongtang Zhang, JiaXing Qiu, Weiguo Dai · IOP Conference Series Materials Science and Engineering · 2019
As one of the most important component of artificial intelligence, machine learning is getting more and more attention. AdaBoost, a classic machine learning algorithm, is widely used. However, when faced with imbalanced data classification, AdaBoost's recognition rate of minority samples is low due to ignoring class imbalance. In many cases, minority samples are of high value. For this shortage, combining the theory of margin and cost-sensitive idea, a new Boosting algorithm called CMBoost is proposed based on cost-sensitive margin statistical characteristics, which is firstly through optimizing margin statistical characteristics to improve formal algorithm and then extended by cost-sensitive. Experimental results on the UCI dataset show that the CMBoost algorithm is superior to AdaBoost for imbalanced data classification problem.