Sequential skewing: An improved skewing algorithm
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.