Classification ensemble by genetic algorithms

Authors:
Hamid Parvin;Behrouz Minaei;Akram Beigi;Hoda Helmi
Affiliations:
School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran;School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran;School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran;School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
Venue:
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Year:
2011

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Abstract

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.