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 proximate dynamics model for data mining
Expert Systems with Applications: An International Journal
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
Experimental evaluation of two new GEP-based ensemble classifiers
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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Gene expression programming (GEP) is a kind of genotype/phenotype based genetic algorithm. Its successful application in classification rules mining has gained wide interest in data mining and evolutionary computation fields. However, current GEP based classifiers represent classification rules in the form of expression tree, which is less meaningful and expressive than decision tree. What's more, these systems adopt one-against-all learning strategy, i.e. to solve a n-class with n runs, each run solving a binary classification task. In this paper, a GEP decision tree(GEPDT) system is presented, the system can construct a decision tree for classification without priori knowledge about the distribution of data, at the same time, GEPDT can solve a n-class problem in a single run, preliminary results show that the performance of GEP based decision tree is comparable to ID3.