C4.5: programs for machine learning
C4.5: programs for machine learning
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A Comparative Analysis of Methods for Pruning Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enlarging the Margins in Perceptron Decision Trees
Machine Learning
Breeding Decision Trees Using Evolutionary Techniques
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Concept Formation and Decision Tree Induction Using the Genetic Programming Paradigm
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
A Genetic Algorithm-Based Approach for Building Accurate Decision Trees
INFORMS Journal on Computing
Evolutionary learning of linear trees with embedded feature selection
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Inducing oblique decision trees with evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
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
Cellular gene expression programming classifier learning
Transactions on computational collective intelligence V
Does memetic approach improve global induction of regression and model trees?
SIDE'12 Proceedings of the 2012 international conference on Swarm and Evolutionary Computation
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In the paper, a new memetic algorithm for decision tree learning is presented. The proposed approach consists in extending an existing evolutionary approach for global induction of classification trees. In contrast to the standard top-down methods, it searches for the optimal univariate tree by evolving a population of trees. Specialized genetic operators are selectively applied to modify both tree structures and tests in non-terminal nodes. Additionally, a local greedy search operator is embedded into the algorithm, which focusses and speeds up the evolutionary induction. The problem of over-fitting is mitigated by suitably defined fitness function. The proposed method is experimentally validated and preliminary results show that the proposed approach is able to effectively induce accurate and concise decision trees.