Multi-criteria classification: a new scheme for application of dominance-based decision rules

J. Blaszczynski, Salvatore Greco, R. Slowinski

Research output: Contribution to journalArticlepeer-review

Abstract

We are considering the problem of multi-criteria classification. In this problem, a set of "if..., then..." decision rules is used as a preference model to classify objects evaluated by a set of criteria and regular attributes. Given a sample of classification examples, called learning data set, the rules are induced from dominance-based rough approximations of preference-ordered decision classes, according to the Variable Consistency Dominance-based Rough Set Approach (VC-DRSA). The main question to be answered in this paper is how to classify an object using decision rules in situation where it is covered by (i) no rule, (ii) exactly one rule, (iii) several rules. The proposed classification scheme can be applied to both, learning data set (to restore the classification known from examples) and testing data set (to predict classification of new objects). A hypothetical example from the area of telecommunications is used for illustration of the proposed classification method and for a comparison with some previous proposals.
Original languageEnglish
Pages (from-to)1030-1044
Number of pages15
JournalEuropean Journal of Operational Research
Volume181
Issue number3
DOIs
Publication statusPublished - 16 Sep 2007

Fingerprint

Dive into the research topics of 'Multi-criteria classification: a new scheme for application of dominance-based decision rules'. Together they form a unique fingerprint.

Cite this