Information-Based Evaluation Criterion for Classifier's Performance
Machine Learning
An Iterative Growing and Pruning Algorithm for Classification Tree Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric classification using matched binary decision trees
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Machine Learning
Breeding Decision Trees Using Evolutionary Techniques
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Application of Genetic Programming to Induction of Linear Classification Trees
Proceedings of the European Conference on Genetic Programming
Genetic Programming and Simulated Annealing: A Hybrid Method to Evolve Decision Trees
Proceedings of the European Conference on Genetic Programming
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Evaluation of decision trees: a multi-criteria approach
Computers and Operations Research
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
An Experimental Evaluation of Some Classification Methods
Journal of Global Optimization
Metadata and its impact on libraries: Book Reviews
Journal of the American Society for Information Science and Technology
Oblique Multicategory Decision Trees Using Nonlinear Programming
INFORMS Journal on Computing
A hybrid method for solving multi-objective global optimization problems
Journal of Global Optimization
Genetic algorithms, selection schemes, and the varying effects of noise
Evolutionary Computation
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
Hierarchical genetic algorithms operating on populations of computer programs
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Lookahead and pathology in decision tree induction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
What should be minimized in a decision tree?
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Inducing oblique decision trees with evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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This paper investigates the performance of evolutionary algorithms in the optimization aspects of oblique decision tree construction and describes their performance with respect to classification accuracy, tree size, and Pareto-optimality of their solution sets. The performance of the evolutionary algorithms is analyzed and compared to the performance of exhaustive (traditional) decision tree classifiers on several benchmark datasets. The results show that the classification accuracy and tree sizes generated by the evolutionary algorithms are comparable with the results generated by traditional methods in all the sample datasets and in the large datasets, the multiobjective evolutionary algorithms generate better Pareto-optimal sets than the sets generated by the exhaustive methods. The results also show that a classifier, whether exhaustive or evolutionary, that generates the most accurate trees does not necessarily generate the shortest trees or the best Pareto-optimal sets.