We investigate the use of oblivious, read-once decision graphs as structures for representing concepts over discrete domains, and present a bottom-up, hill-climbing algorithm for inferring these structures from labelled instances. The algorithm is robust with respect to irrelevant attributes, and experimental results show that it performs well on problems considered difficult for symbolic induction methods, such as the Monk's problems and parity.
CITATION STYLE
Kohavi, R. (1994). Bottom-up induction of oblivious read-once decision graphs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 784 LNCS, pp. 154–169). Springer Verlag. https://doi.org/10.1007/3-540-57868-4_56
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