Fuzzy correlation and support vector learning approach to multi-categorization of documents

In this paper, we propose a new text categorization method for the multi-class and multi-label problems based on support vector machines in conjunction with fuzzy correlation. Support vector machines (SVMs) are learning systems that use a hypothesis space of linear function in a high dimensional feature space, trained with a learning algorithm from optimization theory that implements a learning bias derived from statistical learning theory. SVMs provide efficient and powerful categorization algorithms, which are capable of dealing with high dimensional input space. In addition to SVM, we use concept of fuzzy correlation, which can measure correlation degree between two-variable or two-attribute. We employ fuzzy correlation to measure correlation between unclassified documents and predefined categories. This way not only solves multi-class classification but also multi-label categorization problems.