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Cited 22 time in webofscience Cited 24 time in scopus
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EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

Authors
Sheikh, Khalid HassanAhmed, ShameemMukhopadhyay, KrishnenduSingh, Pawan KumarYoon, Jin HeeGeem, Zong WooSarkar, Ram
Issue Date
Aug-2020
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Optimization; Feature extraction; Tuning; Evolution (biology); Task analysis; Heuristic algorithms; Emotion recognition; Electrical harmony; feature selection; harmony search; artificial electric field algorithm; meta-heuristic; hybrid optimization; UCI datasets
Citation
IEEE ACCESS, v.8, pp.158125 - 158141
Journal Title
IEEE ACCESS
Volume
8
Start Page
158125
End Page
158141
URI
https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/78288
DOI
10.1109/ACCESS.2020.3019809
ISSN
2169-3536
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.
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