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.)
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
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
Genetic Programming and Simulated Annealing: A Hybrid Method to Evolve Decision Trees
Proceedings of the European Conference on Genetic Programming
A Genetic Algorithm-Based Approach for Building Accurate Decision Trees
INFORMS Journal on Computing
High performance RDMA-based MPI implementation over infiniBand
International Journal of Parallel Programming - Special issue I: The 17th annual international conference on supercomputing (ICS'03)
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
Evolutionary learning of linear trees with embedded feature selection
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Inducing oblique decision trees with evolutionary algorithms
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
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In most of data mining systems decision trees are induced in a top-down manner. This greedy method is fast but can fail for certain classification problems. As an alternative a global approach based on evolutionary algorithms (EAs) can be applied. We developed Global Decision Tree(GDT) system, which learns a tree structure and tests in one run of the EA. Specialized genetic operators are used, which allow the system to exchange parts of trees, generate new sub-trees, prune existing ones as well as change the node type and the tests. The system is able to induce univariate, oblique and mixed decision trees. In the paper, we investigate how the GDTsystem can profit from a parallelization on a compute cluster. Both parallel implementation and distributed version of the induction are considered and significant speedups are obtained. Preliminary experimental results show that at least for certain problems the distributed version of the GDTsystem is more accurate than its panmictic predecessor.