Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
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
Evolving Fuzzy Decision Trees with Genetic Programming and Clustering
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Applying genetic programming technique in classification trees
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on intelligent systems for financial engineering and computational finance
Bioinformatics
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
A comparison of classification accuracy of four genetic programming-evolved intelligent structures
Information Sciences: an International Journal
Evolutionary learning of technical trading rules without data-mining bias
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Improving induction decision trees with parallel genetic programming
EUROMICRO-PDP'02 Proceedings of the 10th Euromicro conference on Parallel, distributed and network-based processing
Structural risk minimization over data-dependent hierarchies
IEEE Transactions on Information Theory
Controlling overfitting in symbolic regression based on a bias/variance error decomposition
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Adaptive distance metrics for nearest neighbour classification based on genetic programming
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
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Decision tree learning is one of the most widely used and practical methods for inductive inference. We present a novel method that increases the generalisation of genetically-induced classification trees, which employ linear discriminants as the partitioning function at each internal node. Genetic Programming is employed to search the space of oblique decision trees. At the end of the evolutionary run, a (1+1) Evolution Strategy is used to geometrically optimise the boundaries in the decision space, which are represented by the linear discriminant functions. The evolutionary optimisation concerns maximising the decision-surface margin that is defined to be the smallest distance between the decision-surface and any of the samples. Initial empirical results of the application of our method to a series of datasets from the UCI repository suggest that model generalisation benefits from the margin maximisation, and that the new method is a very competent approach to pattern classification as compared to other learning algorithms.