Difference between revisions of "Publications:Evolving Committees of Support Vector Machines"
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| − | |Name=Valinicius, D. (Kaunas University of Technology, Department of Applied Electronics, Kaunas, Lithuania);Verikas, Antanas | + | |Name=Valinicius, D. (Kaunas University of Technology, Department of Applied Electronics, Kaunas, Lithuania);Verikas, Antanas (av) (0000-0003-2185-8973) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938));Bacauskiene, Marija (Kaunas University of Technology, Department of Electrical and Control Equipment, Kaunas, Lithuania);Gelzinis, Adas (Kaunas University of Technology, Department of Electrical and Control Equipment, Kaunas, Lithuania) |
|Title=Evolving Committees of Support Vector Machines | |Title=Evolving Committees of Support Vector Machines | ||
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|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:239280}} | |diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:239280}} | ||
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Latest revision as of 21:40, 30 September 2016
| Title | Evolving Committees of Support Vector Machines |
|---|---|
| Author | D. Valinicius and Antanas Verikas and Marija Bacauskiene and Adas Gelzinis |
| Year | 2007 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | Machine Learning and Data Mining in Pattern Recognition, Proceedings |
| Conference | 5th International Conference on Machine Learning and Data Mining in Pattern Recognition, Leipzig, Germany, July 18-20, 2007 |
| DOI | http://dx.doi.org/10.1007/978-3-540-73499-4_20 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:239280 |
| Abstract | The main emphasis of the technique developed in this work for evolving committees of support vector machines (SVM) is on a two phase procedure to select salient features. In the first phase, clearly redundant features are eliminated based on the paired t-test comparing the SVM output sensitivity-based saliency of the candidate and the noise feature. In the second phase, the genetic search integrating the steps of training, aggregation of committee members, and hyper-parameter as well as feature selection into the same learning process is employed. A small number of genetic iterations needed to find a solution is the characteristic feature of the genetic search procedure developed. The experimental tests performed on five real world problems have shown that significant improvements in correct classification rate can be obtained in a small number of iterations if compared to the case of using all the features available. |
