A New Feature Selection Method Based on Frequent and Associated Itemsets For Text Classification

Abstract

Feature selection is one of the major issues in pattern recognition. The quality of selected features is important for classification as the low-quality data can degrade the model construction performance. Due to the difficulty of dealing with the problem that selected features always contain redundant information, this paper focuses on the association analysis theory in data mining to select important features. In this study, a novel Feature Selection method based on Frequent and Associated Itemsets (FS-FAI) for text classification is proposed. FS-FAI seeks to find relevant features and also takes feature interaction into account. Moreover, it uses association as a metric to evaluate the relativity between the target concept and feature(s). To evaluate the efficacy of the proposed method, several experiments were conducted on a BBC data set from the BBC news website. The obtained results were compared to well-known feature selection methods. The reported results demonstrated the effectiveness of the proposed feature selection method in selecting high-quality features and in handling redundant information in text classification.

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“…Feature selection is crucial to prevent the model from overfitting, reduce computational time, improve model accuracy, and it is very beneficial for high-dimensional data [22]. Therefore, features of the dataset must be carefully selected before model training to achieve better results [23].…”