کنفرانس مهندسی برق ایران

صفحه اصلی / سی و سومین کنفرانس بین المللی مهندسی برق

Application of Metaheurestic Optimization Algorithms for Feature Selection in Text Classification

نویسندگان :

Elham Nazari¹ Nafise Haghshenas² Alireza Basiri³ Mohammad Reza Ahmadzadeh⁴

1- دانشگاه صنعتی اصفهان 2- دانشگاه صنعتی اصفهان 3- دانشگاه صنعتی اصفهان 4- دانشگاه صنعتی اصفهان

کلمات کلیدی :

Swarm Intelligence Algorithm،Metaheuristic Optimization،Brain Drain Optimization،Gravitational Search Algorithm،Support Vector Machine،Text Classification

چکیده :

Feature selection plays a crucial role in classification systems by identifying a subset of relevant features to optimize performance. In text classification, where each word or unit of information represents a feature, managing the high dimensionality of the feature space poses significant challenges. This study leverages metaheurestic optimization techniques to address these challenges. By integrating these methods with Support Vector Machines, the proposed approach improves both classification accuracy and efficiency. Experimental results studies the ability of these techniques to significantly reduce computational complexity while preserving or enhancing classification performance, demonstrating its potential as a robust solution for high-dimensional text classification tasks. Furthermore, the results highlight the potential of Brain Drain Optimization as an effective feature selection method for text classification tasks.