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MILP approach to pattern generation in logical analysis of data
- Authors
- Ryoo, Hong Seo; Jang, In-Yong
- Issue Date
- 28-2월-2009
- Publisher
- ELSEVIER
- Keywords
- Mixed 0-1 integer and linear programming; Logical analysis of data; Pattern; Supervised machine learning; Combinatorial optimization
- Citation
- DISCRETE APPLIED MATHEMATICS, v.157, no.4, pp.749 - 761
- Indexed
- SCIE
SCOPUS
- Journal Title
- DISCRETE APPLIED MATHEMATICS
- Volume
- 157
- Number
- 4
- Start Page
- 749
- End Page
- 761
- ISSN
- 0166-218X
- Abstract
- Pattern generation methods for the Logical Analysis of Data (LAD) have been term-enumerative in nature. In this paper, we present a Mixed 0-1 Integer and Linear Programming (MILP) approach that can identify LAD patterns that are optimal with respect to various previously studied and new pattern selection preferences. Via art of formulation, the MILP-based method can generate optimal patterns that also satisfy user-specified requirements on prevalence, homogeneity and complexity. Considering that MILP problems with hundreds of 0-1 variables are easily solved nowadays, the proposed method presents an efficient way of generating useful patterns for LAD. With extensive experiments oil benchmark datasets, we demonstrate the utility of the MILP-based pattern generation. (C) 2008 Elsevier B.V. All rights reserved.
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