2024 : 2 : 27
Parham Moradi

Parham Moradi

Academic rank: Associate Professor
Education: PhD.
ScopusId: 654
Faculty: Faculty of Engineering
Address: Department of Computer Engineering, Faculty of Engineering, University of Kurdistan


A Text Classification Method Based on Combination of Information Gain and Graph Clustering
Feature selection, Information gain, text categorization, FAST algorithm
Journal International Journal of Information and Communication Technology Research
Researchers Parham Moradi ، Fatemeh Zamani ، Shadi Rahimi ، Alireza Abdollahpouri


Text classification has a wide range of applications such as: spam filtering, automated indexing of scientific articles, identifying the genre of documents, news monitoring, and so on. Text datasets usually contain much irrelevant and noisy information which eventually reduces the efficiency and cost of their classification. Therefore, for effective text classification, feature selection methods are widely used to handle the high dimensionality of data. In this paper, a novel feature selection method based on the combination of information gain and FAST algorithm is proposed. In our proposed method, at first, the information gain is calculated for the features and those with higher information gain are selected. The FAST algorithm is then used on the selected features which uses graph-theoretic clustering methods. To evaluate the performance of the proposed method, we carry out experiments on three text datasets and compare our algorithm with several feature selection techniques. The results confirm that the proposed method produces smaller feature subset in shorter time. In addition, the evaluation of a K-nearest neighborhood classifier on validation data show that, the novel algorithm gives higher classification accuracy.