Uncertainty in Artificial Intelligence
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Real-valued All-Dimensions search: Low-overhead rapid searching over subsets of attributes
Andrew Moore, Jeff Schneider
Abstract:
This paper is about searching the combinatorial space of contingency tables during the inner loop of a nonlinear statistical optimization. Examples of this operation in various data analytic communities include searching for nonlinear combinations of attributes that contribute significantly to a regression (Statistics), searching for items to include in a decision list (machine learning) and association rule hunting (Data Mining). This paper investigates a new, efficient approach to this class of problems, called RADSEARCH (Real-valued All-Dimensions-tree Search). RADSEARCH finds the global optimum, and this gives us the opportunity to empirically evaluate the question: apart from algorithmic elegance what does this attention to optimality buy us? We compare RADSEARCH with other recent successful search algorithms such as CN2, PRIM, APriori, OPUS and DenseMiner. Finally, we introduce RADREG, a new regression algorithm for learning real-valued outputs based on RADSEARCHing for high-order interactions.
Keywords:
Pages: 360-369
PS Link:
PDF Link: /papers/02/p360-moore.pdf
BibTex:
@INPROCEEDINGS{Moore02,
AUTHOR = "Andrew Moore and Jeff Schneider",
TITLE = "Real-valued All-Dimensions search: Low-overhead rapid searching over subsets of attributes",
BOOKTITLE = "Proceedings of the Eighteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-02)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2002",
PAGES = "360--369"
}


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