An Improved Genetic Algorithm Based on Chi-Square Crossover for Text Categorization

Text classification has gained importance due to the quickly rising content volume. During the text categorization process, it is necessary to complete tasks including extracting relevant information from different viewpoints, reducing the high feature space, and improving efficiency. Many studies on feature selection have been conducted, but increasing efficiency by reducing features remains a challenge. To evaluate its effectiveness, the Amazon review dataset is used in this proposed work. A chi-square-based enhanced genetic algorithm (CSEGA) approach is used in this paper to achieve the purpose of the study. The execution of the task includes the preprocessing task, followed by the optimization process for the selection of the optimal features. The unique crossover and selection process have made this work superior to other algorithms. With 93.0% accuracy, 94.5% precision, 90.5% recall, and a 92.47% F-score, the proposed approach outperforms other state-of-the art algorithms with fewer features.