Ensemble Data Classification based on Diversity of Classifiers Optimized by Genetic Algorithm

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In this research we propose an ensemble classification technique base on creating classification from a variety of techniques such as decision trees, support vector machines, neural networks and then choosing optimize the appropriate classifiers by genetic algorithm and also combined by a majority vote in order to increase classification accuracy. From classification accuracy test on Australian Credit, German Credit and Bankruptcy Data, we found that the proposed ensemble classification models selected by genetic algorithm yields highest performance and our algorithms are effective in building ensemble.

Info:

Periodical:

Advanced Materials Research (Volumes 433-440)

Edited by:

Cai Suo Zhang

Pages:

6572-6578

Citation:

D. Thammasiri and P. Meesad, "Ensemble Data Classification based on Diversity of Classifiers Optimized by Genetic Algorithm", Advanced Materials Research, Vols. 433-440, pp. 6572-6578, 2012

Online since:

January 2012

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$38.00

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