Learning decision rules in noisy domains
Proceedings of Expert Systems '86, The 6Th Annual Technical Conference on Research and development in expert systems III
International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
On estimating probabilities in tree pruning
EWSL-91 Proceedings of the European working session on learning on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Lower bounds on learning decision lists and trees
Information and Computation
A Comparative Analysis of Methods for Pruning Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Exact Probability Metric for Decision Tree Splitting and Stopping
Machine Learning
A New Criterion in Selection and Discretization of Attributes for the Generation of Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Machine Learning
Simplifying decision trees: A survey
The Knowledge Engineering Review
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A Recursive Partitioning Decision Rule for Nonparametric Classification
IEEE Transactions on Computers
Moving towards efficient decision tree construction
Information Sciences: an International Journal
LEGAL-tree: a lexicographic multi-objective genetic algorithm for decision tree induction
Proceedings of the 2009 ACM symposium on Applied Computing
Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach
Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach
Introduction to Machine Learning
Introduction to Machine Learning
Evolutionary model tree induction
Proceedings of the 2010 ACM Symposium on Applied Computing
A new node splitting measure for decision tree construction
Pattern Recognition
Lexicographic multi-objective evolutionary induction of decision trees
International Journal of Bio-Inspired Computation
The attribute selection problem in decision tree generation
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Evolutionary model trees for handling continuous classes in machine learning
Information Sciences: an International Journal
Towards the automatic design of decision tree induction algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Information Sciences: an International Journal
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This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Top-down decision-tree algorithms are of great importance, considering their ability to provide an intuitive and accurate knowledge representation for classification problems. The automatic design of these algorithms seems timely, given the large literature accumulated over more than 40 years of research in the manual design of decision-tree induction algorithms. The proposed hyper-heuristic evolutionary algorithm, HEAD-DT, is extensively tested using 20 public UCI datasets and 10 microarray gene expression datasets. The algorithms automatically designed by HEAD-DT are compared with traditional decision-tree induction algorithms, such as C4.5 and CART. Experimental results show that HEAD-DT is capable of generating algorithms which are significantly more accurate than C4.5 and CART.