Article ID Journal Published Year Pages File Type
391633 Information Sciences 2014 13 Pages PDF
Abstract

This paper presents a very efficient method for establishing nonlinear combinations of variables from small to big data for use in later processing (e.g., regression, classification, etc.). Variables are first partitioned into subsets each of which has a linguistic term (called a causal condition) associated with it. Our Causal Combination Method uses fuzzy sets to model the terms and focuses on interconnections (causal combinations) of either a causal condition or its complement, where the connecting word is AND which is modeled using the minimum operation. Our Fast Causal Combination Method is based on a novel theoretical result, leads to an exponential speedup in computation and lends itself to parallel and distributed processing; hence, it may be used on data from small to big.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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