Multi-pattern generation framework for logical analysis of data

Chun-An Chou (), Tibérius O. Bonates, Chungmok Lee and Wanpracha Art Chaovalitwongse
Additional contact information
Chun-An Chou: SUNY Binghamton
Tibérius O. Bonates: Federal University of Ceara
Chungmok Lee: Hankuk University of Foreign Studies
Wanpracha Art Chaovalitwongse: University of Washington

Annals of Operations Research, 2017, vol. 249, issue 1, No 17, 329-349

Abstract: Abstract Logical analysis of data (LAD) is a rule-based data mining algorithm using combinatorial optimization and boolean logic for binary classification. The goal is to construct a classification model consisting of logical patterns (rules) that capture structured information from observations. Among the four steps of LAD framework (binarization, feature selection, pattern generation, and model construction), pattern generation has been considered the most important step. Combinatorial enumeration approaches to generate all possible patterns were mostly studied in the literature; however, those approaches suffered from the computational complexity of pattern generation that grows exponentially with data (feature) size. To overcome the problem, recent studies proposed column generation-based approaches to improve the efficacy of building a LAD model with a maximum-margin objective. There was still a difficulty in solving subproblems efficiently to generate patterns. In this study, a new column generation framework is proposed, in which a new mixed-integer linear programming approach is developed to generate multiple patterns having maximum coverage in subproblems at each iteration. In addition to the maximum-margin objective, we propose an alternative objective (minimum-pattern) to solve the LAD problem as a minimum set covering problem. The proposed approaches are evaluated on the datasets from the University of California Irvine Machine Learning Repository. The computational experiments provide comparable performances compared with previous LAD and other well-known classification algorithms.

Keywords: Logical analysis of data; Combinatorial optimization; Column generation; Pattern mining; Classification (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s10479-015-1867-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:249:y:2017:i:1:d:10.1007_s10479-015-1867-8

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-015-1867-8

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().