A neuroevolutionary approach to feature selection using multiobjective evolutionary algorithms
| Autor: | Renê S. Pinto, M. Fernanda P. Costa, Lino Costa, António Gaspar-Cunha |
|---|---|
| Přispěvatelé: | Universidade do Minho |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Multiobjective Optimization
Computer science Training time Evolutionary algorithm Feature selection 02 engineering and technology Machine learning computer.software_genre Classifier (linguistics) Neuroevolutionary 0202 electrical engineering electronic engineering information engineering Ciências Naturais::Matemáticas Artificial neural network business.industry 05 social sciences 050301 education Ciências Naturais::Ciências da Computação e da Informação Neuroevolution Support vector machine Multi-objective optimization Order (business) 020201 artificial intelligence & image processing Artificial intelligence business 0503 education computer |
| Zdroj: | Computational Methods in Applied Sciences ISBN: 9783030574215 |
| Popis: | First Online: 24 November 2020 Feature selection plays a central role in predictive analysis where datasets have hundreds or thousands of variables available. It can also reduce the overall training time and the computational costs of the classifiers used. However, feature selection methods can be computationally intensive or dependent of human expertise to analyze data. This study proposes a neuroevolutionary approach which uses multiobjective evolutionary algorithms to optimize neural network parameters in order to find the best network able to identify the most important variables of analyzed data. Classification is done through a Support Vector Machine (SVM) classifier where specific parameters are also optimized. The method is applied to datasets with different number of features and classes. This work has been supported by FCT - Fundação para a Ciência e Tecnologia in the scope of the projects: PEst-OE/EEI/UI0319/2014, UID/MAT/00013/2013, UID/CEC/00319/2019 and the European project MSCA-RISE-2015, NEWEX, with reference 734205. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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