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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 433-440Ensemble Data Classification based on Diversity of...

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

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Abstract:

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.

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Materials Science and Information Technology

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Periodical:

Advanced Materials Research (Volumes 433-440)

Pages:

6572-6578

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.433-440.6572

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Cite this paper

Online since:

January 2012

Authors:

Dech Thammasiri, Phayung Meesad

Keywords:

Artificial Neural Network (ANN), Bagging, Decision Tree, Genetic Algorithm (GA), Support Vector Machine (SVM)

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