期刊
IEEE ACCESS
卷 8, 期 -, 页码 102629-102645出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2999093
关键词
Ring theory based harmony search; feature selection; harmony search; ring theory based evolutionary algorithm; meta-heuristic; hybrid optimization; UCI datasets
资金
- National Research Foundation of Korea (NRF) Grant through the Korea Government (MSIT) [2020R1A2C1A01011131]
- Energy Cloud Research and Development Program through the National Research Foundation of Korea (NRF) through the Ministry of Science, ICT [2019M3F2A1073164]
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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