Abstract
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.
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© 1994 Springer-Verlag Berlin Heidelberg
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Kohavi, R. (1994). Bottom-up induction of oblivious read-once decision graphs. In: Bergadano, F., De Raedt, L. (eds) Machine Learning: ECML-94. ECML 1994. Lecture Notes in Computer Science, vol 784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57868-4_56
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DOI: https://doi.org/10.1007/3-540-57868-4_56
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