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
Global Induction of Decision Trees: From Parallel Implementation to Distributed Evolution
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
A memetic algorithm for global induction of decision trees
SOFSEM'08 Proceedings of the 34th conference on Current trends in theory and practice of computer science
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
Evolving accurate and compact classification rules with gene expression programming
IEEE Transactions on Evolutionary Computation
Two ensemble classifiers constructed from GEP-induced expression trees
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II
Cellular GEP-induced classifiers
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume PartI
Experimental evaluation of two new GEP-based ensemble classifiers
Expert Systems with Applications: An International Journal
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
Rotation forest with GEP-induced expression trees
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
Cellular gene expression programming classifier learning
Transactions on computational collective intelligence V
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The paper proposes applying Gene Expression Programming (GEP) to induce ensemble classifiers. Four algorithms inducing such classifiers are proposed. The first one, denoted GEPA, based on the Adaboost method, is the two-class specific. The second, denoted MV is based on majority voting learning. Third one, denoted MVI, assumes incremental learning where for some classes more genes may be needed than for other ones. Finally, the last one denoted MVC involves partitioning of the training dataset into clusters prior to expression trees induction. The proposed algorithms were validated experimentally using several datasets.