Abstract
Classification tasks often include, among the large number of features to be processed in the datasets, many irrelevant and redundant ones, which can even decrease the efficiency of classifiers. Feature Selection (FS) is the most common preprocessing technique utilized to overcome the drawbacks of the high dimensionality of datasets and often has two conflicting objectives: The first function aims to maximize the classification performance or reduce the error rate of the classifier. In contrast, the second function is designed to minimize the number of features. However, the majority of wrapper FS techniques are developed for single-objective scenarios. Multi-verse optimizer (MVO) is considered as one of the well-regarded optimization approaches in recent years. In this paper, the binary multi-objective variant of MVO (MOMVO) is proposed to deal with feature selection tasks. The standard MOMVO suffers from local optima stagnation, so we propose an improved binary MOMVO to deal with this issue using the memory concept and personal best of the universes. The experimental results and comparisons indicate that the proposed binary MOMVO approach can effectively eliminate irrelevant and/or redundant features and maintain a minimum classification error rate when dealing with different datasets compared with the most popular feature selection techniques. Furthermore, the 14 benchmark datasets showed that the proposed approach outperforms the stat-of-art multi-objective optimization algorithms for feature selection.
Highlights
Data mining is the process of extracting valuable knowledge, and interesting patterns embedded in different data sources [1]
Please note that the numbers in the brackets located at the top of each sub-figure indicate the number of available features and the related classification error values based on all features
The multi-objective variant of MVO (MOMVO) algorithm was designed as a wrapper-based feature selection approach based on utilizing three cosmology concepts: white hole, black hole, and wormhole
Summary
Data mining is the process of extracting valuable knowledge, and interesting patterns embedded in different data sources (e.g., databases and data warehouses) [1]. This paper focuses on FS for classification tasks, where FS methods aim to determine the most informative features in a dataset during a reasonable training time for a specific classifier, simplify the learned models, and improve the performance of the searching and classification engines [21]. This paper proposes an efficient binary Multi-objective MVO optimizer with personal best to improve the efficacy of the basic MVO to handle the feature selection tasks for the first time in literature. The proposed approaches have been tested on fourteen real benchmarks datasets with different settings and characteristics to show their efficiency for feature selection tasks. The rest of this paper is organized as follows: Section 2 presents the review of the related works about multi-objective feature selection algorithms.
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