A novel ensemble algorithm for biomedical classification based on Ant Colony Optimization

Authors:
Lei Shi;Lei Xi;Xinming Ma;Mei Weng;Xiaohong Hu
Affiliations:
Agronomy College, HeNan Agricultural University, Zhengzhou 450002, China and The Incubation Base of National Key Laboratory for Physiological Ecology and Genetic Improvement of Food Crops in Henan ...;College of Information and Management Science, HeNan Agricultural University, Zhengzhou 450002, China;Agronomy College, HeNan Agricultural University, Zhengzhou 450002, China and The Incubation Base of National Key Laboratory for Physiological Ecology and Genetic Improvement of Food Crops in Henan ...;College of Information and Management Science, HeNan Agricultural University, Zhengzhou 450002, China;College of Information and Management Science, HeNan Agricultural University, Zhengzhou 450002, China
Venue:
Applied Soft Computing
Year:
2011

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Abstract

Abstract: One of the major tasks in biomedicine is the classification and prediction of biomedical data. Ensemble learning is an effective method to significantly improve the generalization ability of classification and thus have obtained more and more attentions in the biomedicine community. However, most existing techniques in ensemble learning employ all the trained component classifiers to constitute ensembles, which are sometimes unnecessarily large and can lead to extra memory costs and computational times. For improving the generalization ability and efficiency of ensemble for biomedical classification, an Ant Colony Optimization and rough set based ensemble approach is proposed in this paper. Ant Colony Optimization and rough set theory are incorporated to select a subset of all the trained component classifiers for aggregation. Experiment results show that compared with existing methods, it not only decreases the size of ensemble, but also obtains higher prediction performance.