1. A multi-granularity ensemble classification algorithm for imbalanced data.
- Author
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CHEN Li-fang, DAI Qi, and ZHAO Jia-liang
- Abstract
To address the problems of low accuracy, poor stability and weak generalization ability used in the traditional model when solving the problem of imbalanced data classification, a sequential three-way derision multi-granulation ensemble classification algorithm is proposed. A binary relationship is adopted to realize the dynamic division of the granular layer. The threshold value is calculated according to the cost matrix and a multilayer granular structure is constructed. The data of each granular layer is divided into a positive domain, a boundary domain, and a negative domain, and the derision on each granular layer is recombined according to positive and negative domains, positive and boundary domains, and negative and boundary domains to form a new data subset. A base is built on each data subset to achieve the ensemble classification of imbalanced data. Simulation results show that the algorithm can effectively reduce the imbalance ratio of data subsets and improve the difference of the base classify in ensemble earning. Under the two evaluation indexes of G-rriean and F-measure-i, the classification performance is better or partially better than other ensemble classification algorithms. The new algorithm effectively improves the classification accuracy and stability of the classification model, and provides new research thoughts for ensemble earning of imbalanced data sets. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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