RF ensemble novelties based on optimized & backpropagated NNs
| dc.contributor.author | Koyuncu H. | |
| dc.contributor.author | Ceylan R. | |
| dc.date.accessioned | 2020-03-26T19:43:57Z | |
| dc.date.available | 2020-03-26T19:43:57Z | |
| dc.date.issued | 2017 | |
| dc.department | Selçuk Üniversitesi | en_US |
| dc.description.abstract | This paper presents a classifier model based on Rotation Forest (RF) ensemble structure for biomedical data classification. Classifiers based on RF are generally implemented by using Decision Trees. In this study, optimized Neural Network (NN) is preferred as being the base classifier in RF so as to achieve higher classification performance. Two optimization techniques, Artificial Bee Colony Optimization (ABC) and Particle Swarm Optimization (PSO), are utilized to improve the performance of NN for escaping from local minima. In this way, PSO-NN and ABC-NN based RF structures are designed, and they are called as RF (PSO-NN) and RF (ABC-NN), respectively. In these classifiers, initial weights of NNs are found by using PSO or ABC algorithms. The implemented classifiers based on RF are applied to biomedical datasets (Wisconsin Breast Cancer and Pima Indian Diabetes) that are taken from UCI Machine Learning Repository. Furthermore, fourteen different ensemble structures are generated using these algorithms to prove the superiority of the proposed method. When the results are examined using several performance metrics, it is seen that RF (ABC-NN) classifier achieves to more reliable and better results than other classifiers. | en_US |
| dc.description.sponsorship | Manuscript received May 22, 2017; revised August 5, 2017. This work was supported by the Coordinatorship of Selcuk University’s Scientific Research Projects. | en_US |
| dc.identifier.doi | 10.18178/ijmlc.2017.7.4.624 | en_US |
| dc.identifier.endpage | 84 | en_US |
| dc.identifier.issn | 2010-3700 | en_US |
| dc.identifier.issue | 4 | en_US |
| dc.identifier.scopusquality | N/A | en_US |
| dc.identifier.startpage | 76 | en_US |
| dc.identifier.uri | https://dx.doi.org/10.18178/ijmlc.2017.7.4.624 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12395/35789 | |
| dc.identifier.volume | 7 | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | International Association of Computer Science and Information Technology | en_US |
| dc.relation.ispartof | International Journal of Machine Learning and Computing | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.selcuk | 20240510_oaig | en_US |
| dc.subject | Artificial bee colony optimization | en_US |
| dc.subject | Biomedical data classification | en_US |
| dc.subject | Neural networks | en_US |
| dc.subject | Particle swarm optimization | en_US |
| dc.subject | Rotation forest | en_US |
| dc.title | RF ensemble novelties based on optimized & backpropagated NNs | en_US |
| dc.type | Article | en_US |
