C4.5: programs for machine learning
C4.5: programs for machine learning
Recursive Automatic Bias Selection for Classifier Construction
Machine Learning - Special issue on bias evaluation and selection
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
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
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
Application of Genetic Programming to Induction of Linear Classification Trees
Proceedings of the European Conference on Genetic Programming
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
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
Inducing oblique decision trees with evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Evolutionary Induction of Decision Trees for Misclassification Cost Minimization
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Information Sciences: an International Journal
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In the paper, a new evolutionary algorithm (EA) for mixed tree learning is proposed. In non-terminal nodes of a mixed decision tree different types of tests can be placed, ranging from a typical univariate inequality test up to a multivariate test based on a splitting hyperplane. In contrast to classical top-down methods, our system searches for an optimal tree in a global manner, i.e. it learns a tree structure and tests in one run of the EA. Specialized genetic operators allow for generating new sub-trees, pruning existing ones as well as changing the node type and the tests. The proposed approach was experimentally verified on both artificial and real-life data and preliminary results are promising.