C4.5: programs for machine learning
C4.5: programs for machine learning
A database perspective on knowledge discovery
Communications of the ACM
Fast discovery of association rules
Advances in knowledge discovery and data mining
Computational learning theory and natural learning systems: Volume IV
On the boosting ability of top-down decision tree learning algorithms
Journal of Computer and System Sciences
Efficient mining of association rules using closed itemset lattices
Information Systems
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Optimal conversion of extended-entry decision tables with general cost criteria
Communications of the ACM
The synthetic approach to decision table conversion
Communications of the ACM
Communications of the ACM
Machine Learning
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Building Decision Trees with Constraints
Data Mining and Knowledge Discovery
Tree Induction for Probability-Based Ranking
Machine Learning
Inference for the Generalization Error
Machine Learning
Summarizing itemset patterns: a profile-based approach
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Machine Learning
IEEE Transactions on Computers
An Algorithm for Constructing Optimal Binary Decision Trees
IEEE Transactions on Computers
Skewing: an efficient alternative to lookahead for decision tree induction
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Oversearching and layered search in empirical learning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Integrating decision tree learning into inductive databases
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Bayes optimal classification for decision trees
Proceedings of the 25th international conference on Machine learning
Information theory-based code optimization of matrix elements for overall rotation angular momenta
BIOCOMPUCHEM'09 Proceedings of the 3rd WSEAS International Conference on Computational Chemistry
Towards a general framework for data mining
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Three naive Bayes approaches for discrimination-free classification
Data Mining and Knowledge Discovery
WSEAS Transactions on Information Science and Applications
A fast calculation of metric scores for learning Bayesian network
International Journal of Automation and Computing
Decision trees: a recent overview
Artificial Intelligence Review
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We present DL8, an exact algorithm for finding a decision tree that optimizes a ranking function under size, depth, accuracy and leaf constraints. Because the discovery of optimal trees has high theoretical complexity, until now few efforts have been made to compute such trees for real-world datasets. An exact algorithm is of both scientific and practical interest. From a scientific point of view, it can be used as a gold standard to evaluate the performance of heuristic constraint-based decision tree learners and to gain new insight in traditional decision tree learners. From the application point of view, it can be used to discover trees that cannot be found by heuristic decision tree learners. The key idea behind our algorithm is that there is a relation between constraints on decision trees and constraints on itemsets. We show that optimal decision trees can be extracted from lattices of itemsets in linear time. We give several strategies to efficiently build these lattices. Experiments show that under the same constraints, DL8 obtains better results than C4.5, which confirms that exhaustive search does not always imply overfitting. The results also show that DL8 is a useful and interesting tool to learn decision trees under constraints.