A double-elimination-tournament-based competitive co-evolutionary artificial neural network classifier
| Autor: | Shing Chiang Tan, Bee Yan Hiew, Way Soong Lim |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Artificial neural network
business.industry Computer science 020209 energy Cognitive Neuroscience Computer Science::Neural and Evolutionary Computation Data classification 02 engineering and technology Machine learning computer.software_genre Artificial neural network classifier Backpropagation Computer Science Applications ComputingMethodologies_PATTERNRECOGNITION Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Tournament Local search (optimization) Artificial intelligence business GeneralLiterature_REFERENCE(e.g. dictionaries encyclopedias glossaries) computer Classifier (UML) |
| Zdroj: | Neurocomputing. 249:345-356 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2016.11.082 |
| Popis: | This paper presents a competitive co-evolutionary (ComCoE) that engages a double elimination tournament (DET) to evolve artificial neural networks (ANNs) for undertaking data classification problems. The proposed model performs a global search by a ComCoE approach to find near optimal solutions. During the global search process, two populations of different ANNs compete and fitness evaluation of each ANN is made in a subjective manner based on their participations throughout a DET which promotes competitive interactions among individual ANNs. The adaptation and fitness evaluation processes drive the global search for a more competent ANN classifier. A winning ANN is identified from the global search. Then, the Scaled Conjugate Backpropagation algorithm, which is a local search, is performed to further train the winning ANN to obtain a precise solution. The performance of the proposed classification model is evaluated rigorously; its performance is compared with the baseline ANNs of the proposed model as well as other classifiers. The results indicate that the proposed model could construct an ANN which could produce high classification accuracy rates with a compact network structure. |
| Databáze: | OpenAIRE |
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