Communications of the ACM
On the learnability of Boolean formulae
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
A Further Comparison of Splitting Rules for Decision-Tree Induction
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
A threshold of ln n for approximating set cover (preliminary version)
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Data mining criteria for tree-based regression and classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Machine Learning
A Process-Oriented Heuristic for Model Selection
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Using a Permutation Test for Attribute Selection in Decision Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
On the Power of Decision Lists
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Boosting Using Branching Programs
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
On domain-partitioning induction criteria: worst-case bounds for the worst-case based
Theoretical Computer Science
A Real generalization of discrete AdaBoost
Artificial Intelligence
Intrinsic Geometries in Learning
Emerging Trends in Visual Computing
A Real generalization of discrete AdaBoost
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Real boosting a la carte with an application to boosting oblique decision trees
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Recent advances in the study of voting classification algorithms have brought empirical and theoretical results clearly showing the discrimination power of ensemble classifiers. It has been previously argued that the search of this classification power in the design of the algorithms has marginalized the need to obtain interpretable classifiers. Therefore, the question of whether one might have to dispense with interpretability in order to keep classification strength is being raised in a growing number of machine learning or data mining papers. The purpose of this paper is to study both theoretically and empirically the problem. First, we provide numerous results giving insight into the hardness of the simplicity-accuracy tradeoff for voting classifiers. Then we provide an efficient "top-down and prune" induction heuristic, WIDC, mainly derived from recent results on the weak learning and boosting frameworks. It is to our knowledge the first attempt to build a voting classifier as a base formula using the weak learning framework (the one which was previously highly successful for decision tree induction), and not the strong learning framework (as usual for such classifiers with boosting-like approaches). While it uses a well-known induction scheme previously successful in other classes of concept representations, thus making it easy to implement and compare, WIDC also relies on recent or new results we give about particular cases of boosting known as partition boosting and ranking loss boosting. Experimental results on thirty-one domains, most of which readily available, tend to display the ability of WIDC to produce small, accurate, and interpretable decision committees.