Dynamic Butterfly Optimization Algorithm for Feature Selection
| Autor: | Mohammad Tubishat, Toqir Ahmad Rana, Maen Al-Rashdan, Seyedali Mirjalili, Mohammed Ali Al-Garadi, Mohammed Alswaitti |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Computer science Feature extraction MathematicsofComputing_NUMERICALANALYSIS Feature selection 02 engineering and technology computer.software_genre Transfer function Local optimum feature selection 0202 electrical engineering electronic engineering information engineering General Materials Science Local search (optimization) Butterfly optimization algorithm business.industry General Engineering 020206 networking & telecommunications Statistical classification Butterfly 020201 artificial intelligence & image processing Data mining lcsh:Electrical engineering. Electronics. Nuclear engineering business Classifier (UML) computer lcsh:TK1-9971 local search algorithm based on mutation |
| Zdroj: | IEEE Access, Vol 8, Pp 194303-194314 (2020) |
| ISSN: | 2169-3536 |
| Popis: | Feature selection represents an essential pre-processing step for a wide range of Machine Learning approaches. Datasets typically contain irrelevant features that may negatively affect the classifier performance. A feature selector can reduce the number of these features and maximise the classifier accuracy. This paper proposes a Dynamic Butterfly Optimization Algorithm (DBOA) as an improved variant to Butterfly Optimization Algorithm (BOA) for feature selection problems. BOA represents one of the most recently proposed optimization algorithms. BOA has demonstrated its ability to solve different types of problems with competitive results compared to other optimization algorithms. However, the original BOA algorithm has problems when optimising high-dimensional problems. Such issues include stagnation into local optima and lacking solutions diversity during the optimization process. To alleviate these weaknesses of the original BOA, two significant improvements are introduced in the original BOA: the development of a Local Search Algorithm Based on Mutation (LSAM) operator to avoid local optima problem and the use of LSAM to improve BOA solutions diversity. To demonstrate the efficiency and superiority of the proposed DBOA algorithm, 20 benchmark datasets from the UCI repository are employed. The classification accuracy, the fitness values, the number of selected features, the statistical results, and convergence curves are reported for DBOA and its competing algorithms. These results demonstrate that DBOA significantly outperforms the comparative algorithms on the majority of the used performance metrics. |
| Databáze: | OpenAIRE |
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