EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

Khalid Hassan Sheikh,Jin Hee Yoon,Shameem Ahmed,Zong Woo Geem,Pawan Kumar Singh,Ram Sarkar,Krishnendu Mukhopadhyay

doi:10.1109/access.2020.3019809

Khalid Hassan Sheikh, Jin Hee Yoon + Show 5 more

Open Access

https://doi.org/10.1109/access.2020.3019809

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Abstract

Selecting the most relevant features from a high dimensional dataset is always a challenging task. In this regard, the feature selection (FS) method acts as a solution to this problem mainly in the domain of data mining and machine learning. It aims at improving the performance of a learning model greatly by choosing the relevant features and ignoring the redundant ones. Besides, this also helps to achieve efficient use of space and time by the learning model under consideration. Though over the years, many meta-heuristic algorithms have been proposed by the researchers to solve FS problem, still this is considered as the open research problem due to its enormous challenges. Particularly, these algorithms, at times, suffer from poor convergence because of the improper tuning of exploration and exploitation phases. Here lies the importance of the hybrid meta-heuristics which help to improve the searching capability and convergence rate of the parent algorithms. To this end, the present work introduces a new hybrid meta-heuristic FS model by combining two meta-heuristics - Harmony Search (HS) algorithm and Artificial Electric Field Algorithm (AEFA), which we have named as Electrical Harmony based Hybrid Meta-heurtistic (EHHM). The proposed hybrid meta-heuristic converges faster than its predecessors, thereby ensuring its capability to search efficiently. Usability of EHHM is examined by applying it on 18 standard UCI datasets. Moreover, to prove its supremacy, we have compared it with 10 state-of-the-art FS methods. Link to code implementation of proposed method: khalid0007/Metaheuristic-Algorithms/FS_AEFAhHS.

Highlights

Whenever we have something in abundance, choosing the best one or the required ones becomes very difficult
The performance of the feature selection (FS) algorithm is evaluated on the test set, using the same features decided by the proposed FS algorithm during training
It is compared with 10 state-of-the-art FS methods that include both meta-heuristic and hybrid meta-heuristic algorithms

Summary

Introduction

Whenever we have something in abundance, choosing the best one or the required ones becomes very difficult. The reason for choosing may be we do not need all, or we cannot process all. Selection of the most significant attributes/features from these high dimensional data is a challenging task. To address this problem, FS techniques came into play [3], which is considered as a prepossessing step. FS tries to select a subset of features which is useful for both efficient learning and classification purposes. FS technique tries to get rid of the features which are irrelevant, redundant or act as noise, and do not contribute to the learning process of a model. If we have a ’d’ dimensional feature set, there are 2d numbers of possible feature subsets which makes FS a NP-hard problem

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