Abstract
This paper presents an improved genetic algorithm based feature selection method for multi-class imbalanced data. This method improves the fitness function through using the evaluation criterion EG-mean instead of the global classification accuracy in order to choose the features which are favorable to recognize the minor classes. The method is evaluated using several benchmark data sets, and the experimental results show that, compared with the traditional feature selection method based on genetic algorithm, the proposed method has certain advantages in the size of feature subsets and improves the precision of the minor classes for multi-class imbalanced data sets.
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