A Novel Approach for Text Classification Using Feature Selection Algorithm and Term Weight Measures

Abstract

Text classification is a method for determining the class label of an unknown textual document. In text classification, the vector representation of a document plays a crucial role in enhancing the efficiency of classification process. Several approaches of text classification use content-based features like words for document vector representation. Words with high distinguishing capability increase the performance of the text classification. Therefore, recognizing such words from a huge number of words is an essential step in text classification. This problem of high dimensional is solved with the help of feature selection methods. In the literature, several feature selection methods are proposed by the researchers based on the information of term distributions in various classes of dataset. In this chapter, we developed an approach for text classification (TC) by combining feature selection algorithm (FSA) and term weight measures (TWMs), in which a new feature selection method is developed to delete redundant features and for selecting relevant features. The recognized features are utilized for expressing the documents as vectors. The value of term in representation of vector is calculated by using TWM. In the proposed approach, a new Term Weight Measure is developed and compared the performance of proposed TWM with several well-known TWMs. Six different classification algorithms namely support vector machine (SVM), decision tree (DT), Naïve Bayes (NB), k-nearest neighbour (KNN), logistic regression (LR), and random forest (RF) are used for generating the model for classification. The experiment is performed on six benchmark datasets in the field of TC. The results showed that the proposed approach showed best accuracies for TC on six datasets compared with different works in the domain of TC.