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. © 2011 Springer-Verlag.
CITATION STYLE
Parvin, H., Minaei, B., Beigi, A., & Helmi, H. (2011). Classification ensemble by genetic algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6593 LNCS, pp. 391–399). https://doi.org/10.1007/978-3-642-20282-7_40
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