<![CDATA[Text classification is an important task in natural language processing that faces the main challenge of high dimensionality in the representation of text features, such as Bag-of-Words and TF-IDF. High dimensionality leads to data sparsity, increased computational complexity, and decreased model performance due to irrelevant and redundant features. Therefore, an effective feature selection method is needed to improve the accuracy and efficiency of text classification. This study aims to develop an optimal feature selection method by integrating Genetic Algorithm (GA) as a wrapper method, Linear Discriminant Analysis (LDA) for dimension reduction, and k-Nearest Neighbors (k-NN) as a classifier in a high-dimensional text classification pipeline. GA is used to explore a subset of features in an adaptive and global manner, LDA transforms features into spaces with maximum class separability, and k-NN classifies based on proximity within optimized feature spaces. Experiments were conducted on three standard text datasets, namely 20 Newsgroups, Reuters-21578, and BBC News, with a comprehensive evaluation using a variety of preprocessing configurations and feature representations. The results showed that the GA+LDA+k-NN approach significantly improved the classification accuracy compared to traditional feature selection methods such as Chi-Square, Information Gain, and Mutual Information. This approach is also more robust against noise and is able to handle minority classes better. These findings confirm the urgency of using evolutionary optimization-based feature selection methods in high-dimensional text classification, which not only improves model performance but also provides a practical solution for large-scale and complex text data processing. Thus, this research makes an important contribution both theoretically and applicatively in the development of modern text classification systems. ]]>
