Improved Binary Sailfish Optimizer Based on Adaptive β-Hill Climbing for Feature Selection
| Autor: | Kushal Kanti Ghosh, Shameem Ahmed, Ram Sarkar, Zong Woo Geem, Pawan Kumar Singh |
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| Rok vydání: | 2020 |
| Předmět: |
Source code
General Computer Science biology Computer science media_common.quotation_subject Feature vector General Engineering Binary number 020206 networking & telecommunications Feature selection 02 engineering and technology Learning models Sailfish biology.organism_classification 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing General Materials Science Hill climbing Algorithm media_common Sigmoid transfer function |
| Zdroj: | IEEE Access. 8:83548-83560 |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2020.2991543 |
| Popis: | Feature selection (FS), an important pre-processing step in the fields of machine learning and data mining, has immense impact on the outcome of the corresponding learning models. Basically, it aims to remove all possible irrelevant as well as redundant features from a feature vector, thereby enhancing the performance of the overall prediction or classification model. Over the years, meta-heuristic optimization techniques have been applied for FS, as these are able to overcome the limitations of traditional optimization approaches. In this work, we introduce a binary variant of the recently-proposed Sailfish Optimizer (SFO), named as Binary Sailfish (BSF) optimizer, to solve FS problems. Sigmoid transfer function is utilized here to map the continuous search space of SFO to a binary one. In order to improve the exploitation ability of the BSF optimizer, we amalgamate another recently proposed meta-heuristic algorithm, namely adaptive β-hill climbing (AβHC) with BSF optimizer. The proposed BSF and AβBSF algorithms are applied on 18 standard UCI datasets and compared with 10 state-of-the-art meta-heuristic FS methods. The results demonstrate the superiority of both BSF and AβBSF algorithms in solving FS problems. The source code of this work is available in https://github.com/Rangerix/MetaheuristicOptimization. |
| Databáze: | OpenAIRE |
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