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 novel multivariate filter method for feature selection in text classification problems
A novel multivariate filter method for feature selection in text classification problems
Journal Engineering Applications of Artificial Intelligence
Researchers Parham Moradi ، Mahdi Jalili ، Fardin Ahmadizar ، Mahdieh Labani


With increasing number of documents in digital format, automatic text categorization has become a crucial task in pattern recognition problems. To ease the classification task, feature selection methods have been introduced to reduce the dimensionality of the feature space, and thus improve the classification performance. In this paper a novel filter method for feature selection, called Multivariate Relative Discrimination Criterion (MRDC), is proposed for text classification. The proposed method focuses on the reduction of redundant features using minimal-redundancy and maximal-relevancy concepts. To this end, the proposed method takes into account document frequencies for each term, while estimating their usefulness. The proposed method not only selects the features with maximum relevancy, but also the redundancy between them is takes into account using a correlation metric. MRDC does not employ any learning algorithm to evaluate the usefulness of the selected features, and thus it can be categorized as a filter method. In order to assess the effectiveness of the proposed method, several experiments are performed on three real-world datasets. The obtained results are compared to the state-of-the-art filter methods. The reported results show that in most cases MRDC results in better classification performance than others.