Multi-label text categorization based on feature optimization using ant colony optimization and relevance clustering technique

Feature optimization and feature selection play an important role multi-label text categorization. In multi-label text categorization multiple features share a common class and the process of classification suffered a problem of selection of relevance feature for the classification. In this paper proposed feature optimization based multi-label text categorization. The process of feature optimization is done by ant colony optimization. The ant colony optimization accrued the relevant common feature of document to class. For the process of classification used cluster mapping classification technique. The feature optimization process reduces the loss of data during the transformation of feature mapping during the classification. For the validation of proposed algorithm used some standard dataset such as webpage data, medical search data and RCV1 dataset. Our empirical evaluation shows that proposed algorithm is better than fuzzy relevance technique and other classification technique.