期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2016
卷号：91
期号：2
出版社：Journal of Theoretical and Applied
摘要：Text categorization is a significant approach to manage the increasing text data on the Internet and is a significant research issue since the out bursting of digital and online web where numerous documents are available online and has been increased greatly in recent years. In this paper, a modified version of Table aided Matching algorithm for text categorization is proposed. This approach addressed the issue of huge dimension in order to maximize the computational efficiency and accuracy. The genetic algorithm has the ability to solve this approach. Thus, prior to classification, the dimensionality reduction technique is employed where the size of the documents in each profile is minimized. The performance evaluation of the suggested approach is matched with the two existing classification methodologies and has been demonstrated that the proposed approach has better results matched with existing approaches.
