On Pareto-Optimal Boolean Logical Patterns for Numerical Data

Cui Guo and Hong Seo Ryoo

Applied Mathematics and Computation, 2021, vol. 403, issue C

Abstract: This paper clarifies the difference between intrinsically 0–1 data and binarized numerical data for Boolean logical patterns and strengthens mathematical results and methods from the literature on Pareto-optimal LAD patterns. Toward this end, we select suitable pattern definitions from the literature and adapt them with attention given to unique characteristics of individual patterns and the disparate natures of Boolean and numerical data. Next, we propose a set of revised criteria and definitions by which useful LAD patterns are clearly characterized for both 0–1 and real-valued data. Furthermore, we fortify recent pattern generation optimization models and demonstrate how earlier results on Pareto-optimal patterns can be adapted in accordance with revised pattern definitions. A numerical study validates practical benefits of the results of this paper through optimization-based pattern generation experiments.

Keywords: Logical analysis of data; Boolean logical pattern; Pareto-optimal pattern; Knowledge discovery; Supervised learning (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:403:y:2021:i:c:s0096300321002010

DOI: 10.1016/j.amc.2021.126153

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