Unsupervised text feature selection by binary fire hawk optimizer for text clustering

Feature selection plays a critical role in reducing high-dimensional feature space in machine learning applications without affecting the accuracy of performance. The feature selection problem has been extensively studied in the literature. Nevertheless, few studies have been conducted on unsupervised text feature selection because of the absence of feature class labels and local optimization limitations. For that, this paper proposes three binary versions of the fire hawk optimizer based on different transfer functions for unsupervised text feature selection that can be used to select the most informative features for text clustering. The internal feature subset was evaluated using a mean absolute difference filter. The performance of the proposed methods is tested and compared with other state-of-the-art metaheuristic algorithms on several different benchmark text datasets from different sources using various evaluation metrics. The K-means clustering is applied to cluster documents based on the features selected by the methods. The results of the experiments indicate the effectiveness of the proposed method based on the S-shaped transfer function for improving the performance of document clustering compared with other feature selection methods.