C4.5: programs for machine learning
C4.5: programs for machine learning
Skewing: an efficient alternative to lookahead for decision tree induction
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Generalized skewing for functions with continuous and nominal attributes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Why skewing works: learning difficult Boolean functions with greedy tree learners
ICML '05 Proceedings of the 22nd international conference on Machine learning
Anytime Learning of Decision Trees
The Journal of Machine Learning Research
Any time induction of decision trees: an iterative improvement approach
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Exploiting Product Distributions to Identify Relevant Variables of Correlation Immune Functions
The Journal of Machine Learning Research
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This paper extends previous work on the Skewing algorithm, a promising approach that allows greedy decision tree induction algorithms to handle problematic functions such as parity functions with a lower run-time penalty than Lookahead. A deficiency of the previously proposed algorithm is its inability to scale up to high dimensional problems. In this paper, we describe a modified algorithm that scales better with increasing numbers of variables. We present experiments with randomly generated Boolean functions that evaluate the algorithm's response to increasing dimensions. We also evaluate the algorithm on a challenging real world biomedical problem, that of SH3 domain binding. Our results indicate that our algorithm almost always outperforms an information gain-based decision tree learner.