The Strength of Weak Learnability
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
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Machine Learning
Machine Learning
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
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
| Hi-index | 0.00 |
It was previously argued that Decision Tree learning algorithms such as CART or C4.5 can also be useful to build small and accurate Decision Lists. In that paper, we investigate the possibility of using a similar "top-down and prune" scheme to induce formulae from a much different class: Decision Committees. A decision committee contains rules, each of which being a couple (monomial, vector), where the vector's components are highly constrained with respect to classical polynomials. Each monomial is a condition that, when matched by an instance, returns its vector. When each monomial is tested, the sum of the returned vectors is used to take the decision. Decision Trees, Lists and Committees are complementary formalisms for the user: while trees are based on literal ordering, lists are based on monomial ordering, and committees remove any orderings over the tests. Our contribution is a new algorithm, WIDC, which learns using the same "top-down and prune" scheme, but building Decision Committees. Experimental results on twenty-two domains tend to show that WIDC is able to produce small, accurate, and interpretable decision committees.