Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Concept Formation and Decision Tree Induction Using the Genetic Programming Paradigm
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
SECRET: a scalable linear regression tree algorithm
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Top-Down Induction of Model Trees with Regression and Splitting Nodes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolutionary learning of linear trees with embedded feature selection
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Globally induced model trees: an evolutionary approach
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
An evolutionary algorithm for global induction of regression trees with multivariate linear models
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
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 evolutionary algorithm for induction of univariate regression trees is proposed. In contrast to typical top-down approaches it globally searches for the best tree structure and tests in internal nodes. The population of initial trees is created with diverse top-down methods on randomly chosen sub-samples of the training data. Specialized genetic operators allow the algorithm to efficiently evolve regression trees. The complexity term introduced in the fitness function helps to mitigate the over-fitting problem. The preliminary experimental validation is promising as the resulting trees can be significantly less complex with at least comparable performance to the classical top-down counterpart.