A hybrid feature selection method for text classification using a feature-correlation-based genetic algorithm

This paper introduces a new hybrid method to address the issue of redundant and irrelevant features selected by filter-based methods for text classification. The method utilizes an enhanced genetic algorithm called “Feature Correlation-based Genetic Algorithm” (FC-GA). Initially, a feature subset with the highest classification accuracy is selected by a filter-based method, which will be then used by the FC-GA to generate potential solutions by considering the correlation between features that have similar classification weights and avoiding useless random solutions. The encoding process involves assigning a value of 0 to features that provide a high degree of correlation with other features having almost the same classification information beyond a specified context, while features that are lowly correlated retain their initial code of 1. Through iterative optimization using crossover and mutation operators, the algorithm should remove redundant features that provide strong correlations and high redundancy, which could lead to improved classification performance at a lower computation cost. The aim of this study is to improve the efficiency of filter-based methods, incorporate feature correlation information into genetic algorithms, and utilize pre-optimized feature subsets to efficiently identify optimal solutions. To evaluate the effectiveness of the proposed method, SVM and NB classifiers are employed on six public datasets and compared to five well-known and effective filter-based methods. The results indicate that a significant portion (about 50%) of the features selected by reference filter-based methods are redundant. Eliminating those redundant features leads to a significant improvement in classification performance as measured by the micro-f1 measure.