Multi-label text classification using optimised feature sets

Multi-label text classification is the process of assigning multi-labels to an instance. A significant aspect of the text classification problem is the high dimensionality of the data which hinders the performance of the classifier. Hence, feature selection plays a significant role in classification process that removes the irrelevant data. In this paper, wrapper-based hybrid artificial bee colony and bacterial foraging optimisation (HABBFO) approach has been proposed to select the most appropriate feature subset for prediction. Initially, pre-processing such as tokenisation, stop word removal and stemming has been performed to extract the features (words). Experiments are conducted on the benchmark dataset and the results show that the proposed approach achieves better performance compared to the other feature selection techniques.