Hybrid of Harmony Search Algorithm and Ring Theory-Based Evolutionary Algorithm for Feature Selection

Feature Selection (FS) is an important pre-processing step in the fields of machine learning and data mining, which has a major impact on the performance of the corresponding learning models. The main goal of FS is to remove the irrelevant and redundant features, resulting in optimized time and spac...

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Bibliographic Details
Published in:IEEE access Vol. 8; pp. 102629 - 102645
Main Authors: Ahmed, Shameem, Ghosh, Kushal Kanti, Singh, Pawan Kumar, Geem, Zong Woo, Sarkar, Ram
Format: Journal Article
Language:English
Published: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Data mining
Evolutionary algorithms
Evolutionary computation
Feature extraction
Feature selection
Genetic algorithms
harmony search
Heuristic
Heuristic algorithms
Heuristic methods
hybrid optimization
Machine learning
meta-heuristic
Music
Optimization
Optimization techniques
Performance enhancement
ring theory based evolutionary algorithm
Ring theory based harmony search
Search algorithms
Sociology
Statistics
UCI datasets
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Feature Selection (FS) is an important pre-processing step in the fields of machine learning and data mining, which has a major impact on the performance of the corresponding learning models. The main goal of FS is to remove the irrelevant and redundant features, resulting in optimized time and space requirements along with enhanced performance of the learning model under consideration. Many meta-heuristic optimization techniques have been applied to solve FS problems because of its superiority over the traditional optimization approaches. Here, we have introduced a new hybrid meta-heuristic FS model based on a well-known meta-heuristic Harmony Search (HS) algorithm and a recently proposed Ring Theory based Evolutionary Algorithm (RTEA), which we have named as Ring Theory based Harmony Search (RTHS). Effectiveness of RTHS has been evaluated by applying it on 18 standard UCI datasets and comparing it with 10 state-of-the-art meta-heuristic FS methods. Obtained results prove the superiority of RTHS over the state-of-the-art methods considered here for comparison.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2999093