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
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
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
Incremental Learning of Linear Model Trees
Machine Learning
Data Mining with Decision Trees: Theroy and Applications
Data Mining with Decision Trees: Theroy and Applications
An evolutionary algorithm for global induction of regression trees
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Globally induced model trees: an evolutionary approach
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Evolutionary learning of linear trees with embedded feature selection
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Evolutionary optimized forest of regression trees: application in metallurgy
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
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 we present a new evolutionary algorithm for induction of regression trees. In contrast to the typical top-down approaches it globally searches for the best tree structure, tests at internal nodes and models at the leaves. The general structure of proposed solution follows a framework of evolutionary algorithms with an unstructured population and a generational selection. Specialized genetic operators efficiently evolve regression trees with multivariate linear models. Bayesian information criterion as a fitness function mitigate the over-fitting problem. The preliminary experimental validation is promising as the resulting trees are less complex with at least comparable performance to the classical top-down counterpart.