Enhancing Multi-Label Text Classification through Beta Ant Colony Feature Selection

Abstract

The intricacy and high dimensionality of data pose serious obstacles in the quickly developing area of text categorisation, especially in multi-label scenarios where cases may fall into more than one category. This work presents a new method for feature selection in multi-label text classification that incorporates the Beta Ant Colony Optimisation (BACO) algorithm. Our technology preserves interpretability while improving classification performance by efficiently shrinking the feature space. Taking use of ant colonies' cooperative nature, the suggested approach uses a beta distribution to probabilistically direct the selection of relevant traits. Extensive trials on benchmark datasets show that the accuracy, precision, and recall of the BACO-based feature selection much exceeds those of conventional approaches. Furthermore, we examine how certain characteristics affect the categorisation outcomes, providing information about the significance of variables and the connections between different categories. Our study advances multi-label text classification methods and provides a strong foundation for practitioners and academics seeking to increase the efficacy and efficiency of models in many applications.