Compact MILP models for optimal and Pareto-optimal LAD patterns

Cui Guo; Hong Seo Ryoo

doi:10.1016/j.dam.2012.05.006

Compact MILP models for optimal and Pareto-optimal LAD patterns

Cui Guo, Hong Seo Ryoo

Research output: Contribution to journal › Article › peer-review

25 Citations (Scopus)

Abstract

This paper develops MILP models for various optimal and Pareto-optimal LAD patterns that involve at most 2n 01 decision variables, where n is the number of support features for the data under analysis, which usually is small. Noting that the previous MILP pattern generation models are defined in 2n+m 01 variables, where m is the number of observations in the dataset with m≫n in general, the new models are expected to generate useful LAD patterns more efficiently. With experiments on six well-studied machine learning datasets, we first demonstrate the efficiency of the new MILP models and next use them to show different utilities of strong prime patterns and strong spanned patterns in enhancing the overall classification accuracy of a LAD decision theory.

Original language	English
Pages (from-to)	2339-2348
Number of pages	10
Journal	Discrete Applied Mathematics
Volume	160
Issue number	16-17
DOIs	https://doi.org/10.1016/j.dam.2012.05.006
Publication status	Published - 2012 Nov

Bibliographical note

Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Grant Number: 2010-0016571 ).

Keywords

LAD
MILP
Maximum prime pattern
Maximum spanned pattern
Strong prime pattern
Strong spanned pattern

ASJC Scopus subject areas

Discrete Mathematics and Combinatorics
Applied Mathematics

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10.1016/j.dam.2012.05.006

Cite this

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title = "Compact MILP models for optimal and Pareto-optimal LAD patterns",

abstract = "This paper develops MILP models for various optimal and Pareto-optimal LAD patterns that involve at most 2n 01 decision variables, where n is the number of support features for the data under analysis, which usually is small. Noting that the previous MILP pattern generation models are defined in 2n+m 01 variables, where m is the number of observations in the dataset with m≫n in general, the new models are expected to generate useful LAD patterns more efficiently. With experiments on six well-studied machine learning datasets, we first demonstrate the efficiency of the new MILP models and next use them to show different utilities of strong prime patterns and strong spanned patterns in enhancing the overall classification accuracy of a LAD decision theory.",

keywords = "LAD, MILP, Maximum prime pattern, Maximum spanned pattern, Strong prime pattern, Strong spanned pattern",

author = "Cui Guo and Ryoo, {Hong Seo}",

note = "Funding Information: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Grant Number: 2010-0016571 ). ",

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doi = "10.1016/j.dam.2012.05.006",

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AU - Ryoo, Hong Seo

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AB - This paper develops MILP models for various optimal and Pareto-optimal LAD patterns that involve at most 2n 01 decision variables, where n is the number of support features for the data under analysis, which usually is small. Noting that the previous MILP pattern generation models are defined in 2n+m 01 variables, where m is the number of observations in the dataset with m≫n in general, the new models are expected to generate useful LAD patterns more efficiently. With experiments on six well-studied machine learning datasets, we first demonstrate the efficiency of the new MILP models and next use them to show different utilities of strong prime patterns and strong spanned patterns in enhancing the overall classification accuracy of a LAD decision theory.

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KW - Maximum spanned pattern

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Compact MILP models for optimal and Pareto-optimal LAD patterns

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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