A Preliminary Study on Constructing Decision Tree with Gene Expression Programming
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1
GEP-Induced Expression Trees as Weak Classifiers
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
A Family of GEP-Induced Ensemble Classifiers
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Distance guided classification with gene expression programming
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
GEPCLASS: a classification rule discovery tool using gene expression programming
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
A new ensemble method for gold mining problems: Predicting technology transfer
Electronic Commerce Research and Applications
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The paper proposes applying Gene Expression Programming (GEP) to induce ensemble classifiers. Two new algorithms inducing such classifiers are proposed. The proposed ensemble classifiers use two different measures to select genes produced by the Gene Expression Programming procedure. Selection of genes from the set of the non-dominated ones in the process of meta-learning is supported by a genetic algorithm. Integration of genes (i.e. learners) is based on the majority voting. The proposed algorithms were validated experimentally using several datasets and the results were compared with those of other well established classification methods.