Classification Ensemble by Genetic Algorithms
Different classifiers with different characteristics and methodologies can complement each other and cover their internal weaknesses; Thus Classifier ensemble is an important approach to handle the drawback. If an automatic and fast method is obtained to approximate the accuracies of different classifiers on a typical dataset, the learning can be converted to an optimization problem and genetic algorithm is an important approach in this way. We proposed a selection method for classification ensemble by applying GA for improving performance of classification. CEGA is examined on some datasets and it considerably shows improvements.
KeywordsClassifier Selection Classifier Ensemble Genetic Algorithms
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- 4.Saberi, A., Vahidi, M., Minaei-Bidgoli, B.: Learn to Detect Phishing Scams Using Learning and Ensemble Methods. In: IEEE/WIC/ACM International Conference on Intelligent Agent Technology, Workshops (IAT 2007), Silicon Valley, USA, November 2-5, pp. 311–314 (2007)Google Scholar
- 9.Bala, J., De Jong, K., Huang, J., Vafaie, H., Wechsler, H.: Using learning to facilitate the evolution of features for recognizing visual concepts. Evolutionary Computation 4(3) (1997); Special Issue on Evolution, Learning, and Instinct: 100 years of the Baldwin EffectGoogle Scholar
- 14.Gunter, S., Bunke, H.: Creation of classifier ensembles for handwritten word recognition using feature selection algorithms. In: IWFHR (2002)Google Scholar
- 16.Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
- 17.Parvin, H., Alizadeh, H., Minaei-Bidgoli, B.: MKNN: Modified K-Nearest Neighbor. In: WCECS 2008, San Francisco, USA (2008)Google Scholar