Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
C4.5: programs for machine learning
C4.5: programs for machine learning
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Artificial Intelligence in Medicine
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
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
Data mining with a parallel rule induction system based on gene expression programming
International Journal of Innovative Computing and Applications
Cellular gene expression programming classifier learning
Transactions on computational collective intelligence V
Self-organizing ARTMAP rule discovery
Neural Networks
Evolving multi-label classification rules with gene expression programming: a preliminary study
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
| Hi-index | 0.00 |
This work describes the use of a recently proposed technique – gene expression programming – for knowledge discovery in the data mining task of data classification. We propose a new method for rule encoding and genetic operators that preserve rule integrity, and implemented a system, named GEPCLASS. Due to its encoding scheme, the system allows the automatic discovery of flexible rules, better fitted to data. The performance of GEPCLASS was compared with two genetic programming systems and with C4.5, over four data sets in a five-fold cross-validation procedure. The predictive accuracy for the methods compared were similar, but the computational effort needed by GEPCLASS was significantly smaller than the other. GEPCLASS was able to find simple and accurate rules as it can handle continuous and categorical attributes.