An AdaBoost Method with K′K-Means Bayes Classifier for Imbalanced Data

Yanfeng Zhang () and Lichun Wang
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Yanfeng Zhang: Department of Statistics, Beijing Jiaotong University, Beijing 100044, China
Lichun Wang: Department of Statistics, Beijing Jiaotong University, Beijing 100044, China

Mathematics, 2023, vol. 11, issue 8, 1-11

Abstract: This article proposes a new AdaBoost method with k ′ k-means Bayes classifier for imbalanced data. It reduces the imbalance degree of training data through the k ′ k-means Bayes method and then deals with the imbalanced classification problem using multiple iterations with weight control, achieving a good effect without losing any raw data information or needing to generate more relevant data manually. The effectiveness of the proposed method is verified by comparing it with other traditional methods based on numerical experiments. In the NSL-KDD data experiment, the F-score values of each minority class are also greater than the other methods.

Keywords: imbalanced data; naive Bayes; imbalanced classifiers; AdaBoost method (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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