An illustration of variable precision rough sets model: an analysis of the findings of the UK Monopolies and Mergers Commission

Malcolm J. Benyon, Nigel L. Driffield

Research output: Contribution to journalArticle

Abstract

This paper introduces a new technique in the investigation of limited-dependent variable models. This paper illustrates that variable precision rough set theory (VPRS), allied with the use of a modern method of classification, or discretisation of data, can out-perform the more standard approaches that are employed in economics, such as a probit model. These approaches and certain inductive decision tree methods are compared (through a Monte Carlo simulation approach) in the analysis of the decisions reached by the UK Monopolies and Mergers Committee. We show that, particularly in small samples, the VPRS model can improve on more traditional models, both in-sample, and particularly in out-of-sample prediction. A similar improvement in out-of-sample prediction over the decision tree methods is also shown.
Original languageEnglish
Pages (from-to)1739-1759
Number of pages21
JournalComputers and Operations Research
Volume32
Issue number7
DOIs
Publication statusPublished - Jul 2005

Fingerprint

Variable Precision Rough Set
Mergers
Rough Set Theory
Decision tree
Rough set theory
Decision trees
Probit Model
Prediction
Small Sample
Monte Carlo Simulation
Discretization
Model
Economics
Dependent
Monopoly
Rough set
Out-of-sample forecasting

Keywords

  • decision trees
  • monopolies policy
  • object classification
  • rule construction
  • variable precision rough sets model

Cite this

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An illustration of variable precision rough sets model : an analysis of the findings of the UK Monopolies and Mergers Commission. / Benyon, Malcolm J.; Driffield, Nigel L.

In: Computers and Operations Research, Vol. 32, No. 7, 07.2005, p. 1739-1759.

Research output: Contribution to journalArticle

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