Multiple Filter-Based Rankers to Guide Hybrid Grasshopper Optimization Algorithm and Simulated Annealing for Feature Selection With High Dimensional Multi-Class Imbalanced Datasets

作     者：Sharifai, Abdulrauf Garba Zainol, Zurinahni Binti 

作者机构：Univ Sains Malaysia Sch Comp Sci Gelugor 11800 Malaysia Yusuf Maitama Sule Univ Dept Comp Sci Kano 700252 Nigeria 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2021年第9卷

页      面：74127-74142页

核心收录：

基　　金：Universiti Sains Malaysia [1001/PKOMP/8014084] 

主　　题：Optimization Feature extraction Filtering algorithms Classification algorithms Search problems Task analysis Simulated annealing Grasshopper optimization algorithm simulated annealing tournament selection multiple filter-based rankers feature selection hybrid filter-wrapper high-dimensionality imbalanced class distribution 

摘      要：DNA microarray data analysis is infamous due to a massive number of features, imbalanced class distribution, and limited available samples. In this paper, we focus on high-dimensional multi-class imbalanced problems. The high dimensional and multi-class imbalanced problem has posed acute challenges for the conventional classifiers to effectively perform classification tasks on both the minority and majority classes. Numerous efforts have been devoted to addressing either high dimensionality dataset or class imbalance problems. Nonetheless, few methods have been proposed to address the intersection of multi-class imbalanced and high-dimensional problems concurrently due to their intricate interactions. This paper presents novel hybrid algorithms for feature selection with the high dimensional multi-class imbalanced problem using multiple filter-based rankers (MFR) and hybrid Grasshopper optimization algorithm (GOA). The Simulated Annealing (SA) algorithm is incorporated into GOA. SA is used to enhance the best solution found by the GOA algorithm. The aim of using the SA here is to tackle the slow convergence and improve the exploitation by searching the high-quality regions found by the GOA. The experimental results confirm the effectiveness of the proposed methods in improving the classification performance in terms of area under the curve (AUC) compared to other well-known methods, which guarantees the ability of the proposed methods in searching the feature space and identifying very robust and discriminative features that best predict the minority class.