Real boosting a la carte with an application to boosting oblique decision trees

Authors:
Claudia Henry;Richard Nock;Frank Nielsen
Affiliations:
U. Antilles-Guyane, CEREGMIA, Martinique, France;U. Antilles-Guyane, CEREGMIA, Martinique, France;SONY CS Labs Inc., Tokyo, Japan
Venue:
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Year:
2007

Citing 11
Cited 1

Crytographic limitations on learning Boolean formulae and finite automata

STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
The Strength of Weak Learnability

Machine Learning
C4.5: programs for machine learning

C4.5: programs for machine learning
A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Data-dependent structural risk minimisation for perceptron decision trees

NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
On the boosting ability of top-down decision tree learning algorithms

Journal of Computer and System Sciences
Improved Boosting Algorithms Using Confidence-rated Predictions

Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Learning Decision Lists

Machine Learning
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
Inducing interpretable voting classifiers without trading accuracy for simplicity: theoretical results, approximation algorithms, and experiments

Journal of Artificial Intelligence Research
Inducing oblique decision trees with evolutionary algorithms

IEEE Transactions on Evolutionary Computation

Intrinsic Geometries in Learning

Emerging Trends in Visual Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the past ten years, boosting has become a major field of machine learning and classification. This paper brings contributions to its theory and algorithms. We first unify a well-known top-down decision tree induction algorithm due to [Kearns and Mansour, 1999], and discrete AdaBoost [Freund and Schapire, 1997], as two versions of a same higher-level boosting algorithm. It may be used as the basic building block to devise simple provable boosting algorithms for complex classifiers. We provide one example: the first boosting algorithm for Oblique Decision Trees, an algorithm which turns out to be simpler, faster and significantly more accurate than previous approaches.