Full Citation

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

Citation Type: Miscellaneous

Publication Year: 2002

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Abstract: This paper is about searching the combina­ torial space of contingency tables during the inner loop of a nonlinear statistical optimiza­ tion. Examples of this operation in various data analytic communities include search­ ing for nonlinear combinations of attributes that contribute significantly to a regression (Statistics), searching for items to include in a decision list (machine learning) and associ­ ation rule hunting (Data Mining). This paper investigates a new, efficient ap­ proach to this class of problems, called RAD­ SEARCH (Real-valued All-Dimensions-tree Search). RADSEARCH finds the global op­ timum, 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. Fi­ nally, we introduce RADREG, a new regres­ sion algorithm for learning real-valued out­ puts based on RADSEARCHing for high­ order interactions.

Url: https://arxiv.org/pdf/1301.0589.pdf

User Submitted?: No

Authors: Moore, Andrew; Schneider, Jeff

Publisher: School of Computer Science Carnegie Mellon University

Data Collections: IPUMS USA

Topics: Population Data Science

Countries: United States