Multi-criteria classification – A new scheme for application of dominance-based decision rules

• Published in: European journal of operational research Vol. 181; no. 3; pp. 1030 - 1044
• Main Authors: Błaszczyński, Jerzy, Greco, Salvatore, Słowiński, Roman
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Amsterdam Elsevier B.V 16.09.2007; Elsevier; Elsevier Sequoia S.A
• Series: European Journal of Operational Research
• Subjects: Applied sciences; Classification; Classifiers; Computer science; control theory; systems; Data processing. List processing. Character string processing; Datasets; Decision rules; Decision theory. Utility theory; Dominance; Exact sciences and technology; Memory organisation. Data processing; Multi-criteria classification; Multiple criteria decision making; Operational research and scientific management; Operational research. Management science; Rough Sets; Software; Studies; Decision rules; Classifiers; Dominance; Multi-criteria classification; Rough Sets; Classification scheme; Multicriteria analysis; Expert system; Roughness; Set computation; Telecommunication; Classifier; Modeling; Decision rule; Preference; Classification; Set theory
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2006.03.004

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.