Feature Selection by Using Heuristic Methods for Text Classification

Abstract

Feature selection can be defined as the selection of the best subset to represent the data set in machine learning applications, in other words extraction of the unnecessary data that has no effect on the result. In classification problems efficiency and accuracy of the system can be increased when the dimension is reduced by feature selection. In this study, text classifying application is performed by using the data set of "20 News Group" released in Reuters News Agent. The pre-processed news data were converted to vectors by using Doc2Vec method and the data set was created and classified by Naive Bayes method. Subsequently, a subset of the data set was formed by using heuristic methods that were inspired by nature (Whale and Gray Wolf Optimization Algorithms) and Chi-square method for feature selection. Then the reclassification was applied and the results were compared. While the success of the system with 600 features before the feature selection is 0.9214, the performance ratio of the 100 featured models created later is figured higher (0.94095 - 0.93833 - 0.93619).