Published on Wed Dec 12 2012

Real-valued All-Dimensions search: Low-overhead rapid searching over subsets of attributes

Andrew Moore, Jeff Schneider

This paper investigates a new, efficient approach to this class of problems. We compare RADSEARCH with other recent successful search algorithms such as PRIM, APriori, OPUS and DenseMiner. We introduce RADREG, a newression algorithm for learning real-valued outputs based

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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.