The improved AdaBoost algorithms for imbalanced data classification

Class imbalance is one of the most popular and important issues in the domain of classification. The AdaBoost algorithm is an effective solution for classification, but it still needs improvement in the imbalanced data problem. This paper proposes a method to improve the AdaBoost algorithm using the new weighted vote parameters for the weak classifiers. Our proposed weighted vote parameters are determined not only by the global error rate but also by the classification accuracy rate of the positive class, which is our primary interest. The imbalanced index of the data is also a factor in constructing our algorithms. Our proposed algorithms outperform the traditional ones, especially regarding the evaluation criterion of F-1Measure. Theoretic proofs of the advantages of our proposed algorithms are presented. Two kinds of simulated datasets and four real datasets are applied in the experiment as the specific support to our proposed algorithms.