C4.5: programs for machine learning
C4.5: programs for machine learning
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
Tree induction vs. logistic regression: a learning-curve analysis
The Journal of Machine Learning Research
Sequential skewing: an improved skewing algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Skewing: an efficient alternative to lookahead for decision tree induction
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Lookahead and pathology in decision tree induction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Anytime Learning of Decision Trees
The Journal of Machine Learning Research
Exploiting Product Distributions to Identify Relevant Variables of Correlation Immune Functions
The Journal of Machine Learning Research
An efficient approximation to lookahead in relational learners
ECML'06 Proceedings of the 17th European conference on Machine Learning
Particle swarm classification: A survey and positioning
Pattern Recognition
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This paper extends previous work on skewing, an approach to problematic functions in decision tree induction. The previous algorithms were applicable only to functions of binary variables. In this paper, we extend skewing to directly handle functions of continuous and nominal variables. We present experiments with randomly generated functions and a number of real world datasets to evaluate the algorithm's accuracy. Our results indicate that our algorithm almost always outperforms an Information Gain-based decision tree learner.