Artificial bee colony algorithm for feature selection and improved support vector machine for text classification. Issue 3 (19th August 2019)

• Record Type: Journal Article
• Authors: Balakumar, Janani; Mohan, S. Vijayarani
• Is Part Of: Information discovery and delivery. Volume 47:Issue 3(2019)
• Page Start: 154
• Page End: 170
• Publication Date: 2019-08-19
• Subjects: Information technology -- Information science -- Information retrieval -- Information management -- Information systems -- Document management -- Text classification -- Feature selection -- Information gain -- χ2 statistic -- Artificial bee colony -- Support vector machine -- Improved SVM; Information retrieval -- Periodicals; Document delivery -- Periodicals; Digital libraries -- Periodicals; Information storage and retrieval systems -- Periodicals; 025.524
• DOI: 10.1108/IDD-09-2018-0045
• Languages: English
• ISSNs: 2398-6247

Abstract

Abstract : Purpose: Owing to the huge volume of documents available on the internet, text classification becomes a necessary task to handle these documents. To achieve optimal text classification results, feature selection, an important stage, is used to curtail the dimensionality of text documents by choosing suitable features. The main purpose of this research work is to classify the personal computer documents based on their content. Design/methodology/approach: This paper proposes a new algorithm for feature selection based on artificial bee colony (ABCFS) to enhance the text classification accuracy. The proposed algorithm (ABCFS) is scrutinized with the real and benchmark data sets, which is contrary to the other existing feature selection approaches such as information gain and χ 2 statistic. To justify the efficiency of the proposed algorithm, the support vector machine (SVM) and improved SVM classifier are used in this paper. Findings: The experiment was conducted on real and benchmark data sets. The real data set was collected in the form of documents that were stored in the personal computer, and the benchmark data set was collected from Reuters and 20 Newsgroups corpus. The results prove the performance of the proposed feature selection algorithm by enhancing the text document classification accuracy. Originality/value: This paper proposes a new ABCFS algorithm for feature selection, evaluates the efficiency of the ABCFS algorithm and improves the support vector machine. In this paper, the ABCFS algorithm is used to select the features from text (unstructured) documents. Although, there is no text feature selection algorithm in the existing work, the ABCFS algorithm is used to select the data (structured) features. The proposed algorithm will classify the documents automatically based on their content.