Accepted to ECML-94 Bottom-Up Induction of Oblivious Read-Once Decision Graphs
نویسنده
چکیده
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 di cult for symbolic induction methods, such as the Monk's problems and parity.
منابع مشابه
Bottom-Up Induction of Oblivious Read-Once Decision Graphs
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 diicult for symbolic induct...
متن کاملAppears in AAAI-94 Bottom-Up Induction of Oblivious Read-Once Decision Graphs: Strengths and Limitations
We report improvements to HOODG, a supervised learning algorithm that induces concepts from labelled instances using oblivious, read-once decision graphs as the underlying hypothesis representation structure. While it is shown that the greedy approach to variable ordering is locally optimal, we also show an inherent limitation of all bottom-up induction algorithms, including HOODG, that constru...
متن کاملBottom-Up Induction of Oblivious Read-Once Decision Graphs: Strengths and Limitations
We report improvements to HOODG, a supervised learning algorithm that induces concepts from labelled instances using oblivious, read-once decision graphs as the underlying hypothesis representation structure. While it is shown that the greedy approach to variable ordering is locally optimal, we also show an inherent limitation of all bottom-up induction algorithms , including HOODG, that constr...
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We report improvements to HOODG, a supervised learning algorithm that induces concepts from labelled instances using oblivious, read-once decision graphs as the underlying hypothesis representation structure. While it is shown that the greedy approach to variable ordering is locally optimal, we also show an inherent limitation of all bottom-up induction algorithms, including HOODG, that constru...
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We describe a supervised learning algorithm, EODG, that uses mutual information to build an oblivious decision tree. The tree is then converted to an Oblivious read-Once Decision Graph (OODG) by merging nodes at the same level of the tree. For domains that are appropriate for both decision trees and OODGs, performance is approximately the same as that of C4.5, but the number of nodes in the OOD...
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