关键词: 文本分类, 特征选择, 遗传算法, 卡方检验, 初始种群

Abstract: Selecting text features to reduce feature dimensions, improve classification accuracy, and reduce the time consumption of the classification process is undoubtedly a problem that text classification tasks inevitably face and need to solve in the context of big data. In existing text feature selection methods, genetic algorithms (GA) and other optimization algorithms are often used to transform the text feature selection problem into a feature optimization combination problem with classification accuracy as the goal.
Research on text feature selection based on GA has achieved significant results in reducing the optimization range of feature selection, reducing dependence on classification models, and mitigating the adverse effects of genetic randomness such as selection, crossover, and mutation. However, existing research has largely overlooked the randomness of the initial population of GA, which has a negative impact on feature iterative optimization selection. Randomizing the initial population ignores the contribution of feature words to classification, which to some extent increases the difficulty of the algorithm quickly converging near the global optimal solution.
Therefore, this article proposes a text feature selection method CHI_FIPGA that combines CHI square test with a fixed initial population GA. The initial population of GA is set to select feature words with higher CHI values. By selecting different numbers of feature words, the differences between individuals in the initial population are maintained. The classification accuracy of the classification model is used as the fitness, and genetic operations such as selection, crossover, and mutation are performed to iteratively optimize within the range of all feature words.
Multiple Chinese text classification experimental datasets are selected, and performed to preprocess operations on the data, including word segmentation, removal of stop words, matrix transformation, and feature word weighting. Different classification models such as multi-layer perceptron neural networks, random forests, naive Bayes, K-nearest neighbors, decision trees are used to compare optimal solution results with GA, CHI_GA, PSO, and CHI_PSO.
The experimental results indicate that compared with the random initial population GA, the CHI_FIPGA method has an average improvement of 14% in classification accuracy and an average reduction of 66% in the number of feature words. Compared to the CHI_GA method, the average classification accuracy of the CHI_FIPGA method improves by 9%, and the average number of feature words decreases by 36%. Compared with the PSO method, the CHI_FIPGA method has an average improvement of 7% in classification accuracy and an average reduction of 61% in the number of feature words. Compared with the CHI_PSO method, the CHI_FIPGA method has an average improvement of 3.4% in classification accuracy and a 25% reduction in the number of feature words.
CHI_FIPGA is based solely on the CHI method for feature word evaluation and ranking. It attempts to use various filtering methods such as IG, TF-IDF, F-value, etc. to comprehensively evaluate and rank feature words, analyze the differences in the contribution of feature words to different category classifications, and consider the structural characteristics of classification models in the feature word evaluation and ranking process. This is a feasible path to further improving the quality of the fixed initial population.

Key words: text classification, feature selection, genetic algorithm, CHI square test, initial population