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
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
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
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
Evolutionary learning of linear trees with embedded feature selection
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Temperature prediction in electric arc furnace with neural network tree
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
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
Evolutionary design of decision trees for medical application
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Does memetic approach improve global induction of regression and model trees?
SIDE'12 Proceedings of the 2012 international conference on Swarm and Evolutionary Computation
Hi-index | 0.01 |
In the paper we propose a new evolutionary algorithm for induction of univariate regression trees that associate leaves with simple linear regression models. In contrast to typical top-down approaches it globally searches for the best tree structure, tests in internal nodes and models in leaves. The population of initial trees is created with diverse top-down methods on randomly chosen subsamples of the training data. Specialized genetic operators allow the algorithm to efficiently evolve regression trees. Akaike's information criterion (AIC) as the fitness function helps to mitigate the overfitting 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 counterparts.