Hybrid Grasshopper and Chameleon Swarm Optimization Algorithm for Text Feature Selection with Density Peaks Clustering

摘要

Clustering consists of various applications on machine learning, image segmentation, data mining and pattern recognition. The proper selection of clustering is significant in feature selection. Therefore, in this paper, a Text Feature Selection (FS) and Clustering using Grasshopper-Chameleon Swarm Optimization with Density Peaks Clustering algorithm (TFSC-G-CSOA-DPCA) is proposed. Initially, the input features are pre-processed for converting text into numerical form. These preprocessed text features are given to Grasshopper-Chameleon Swarm Optimization Algorithm, which selects important text features. In Grasshopper-Chameleon Swarm Optimization Algorithm, the Grasshopper Optimization Algorithm selects local feature from text document and Chameleon Swarm Optimization Algorithm selects the best global feature from local feature. These important features are tested using density peaks clustering algorithm to maximize the reliability and minimize the computational time cost. The performance of Grasshopper-Chameleon Swarm Optimization Algorithm is analyzed with 20 News groups dataset. Moreover, the performance metrics, like accuracy, precision, sensitivity, specificity, execution time and memory usage are analyzed. The simulation process shows that the proposed TFSC-G-CSOA-DPCA method provides better accuracy of 97.36%, 95.14%, 94.67% and 91.91% and maximum sensitivity of 96.25%, 87.25%, 93.96% and 92.59% compared to the existing methods such as TFSC-BBA-MCL, TFSCMVO-K-Means C, TFSC-GWO-GOA-FCM and TFSC-WM-K-Means C, respectively.