Engen, V., Vincent, J., Schierz, A. C. and Phalp, K. T., 2009. Multi-Objective Evolution Of The Pareto Optimal Set Of Neural Network Classifier Ensembles. In: International Conference of Machine Learning and Cybernetics (ICMLC & ICWAPR)2009., 12-15 July 2009, Baoding, Hebei, China, pp. 74-79.
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Existing research demonstrates that classifier ensem- bles can improve on the performance of the single ‘best’ classifier. However, for some problems, although the ensemble may obtain a lower classification error than any of the base classifiers, it may not provide the de- sired trade-off among the classification rates of differ- ent classes. In many applications, classes are not of equal importance, but the preferred trade-off may be hard to quantify a priori. In this paper, we adopt multi- objective techniques to create Pareto optimal sets of classifiers and ensembles, offering the user the choice of preferred trade-off. We also demonstrate that the common practice of developing a single ensemble from an arbitrary (diverse) selection of base classifiers will be inferior to a large proportion of those classifiers.
|Item Type:||Conference or Workshop Item (Paper)|
|Subjects:||Generalities > Computer Science and Informatics > Artificial Intelligence|
|Group:||School of Design, Engineering & Computing > Smart Technology Research Centre|
School of Design, Engineering & Computing > Software Systems Research Centre
|Deposited By:||Dr Amanda C. Schierz LEFT|
|Deposited On:||09 Dec 2009 17:26|
|Last Modified:||07 Mar 2013 15:18|
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