Rough set based decision support

R. Slowinski, Salvatore Greco, B. Matarazzo

    Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review


    In this chapter, we are concerned with discovering knowledge from data. The aim is to find concise classification patterns that agree with situations that are described by the data. Such patterns are useful for explanation of the data and for the prediction of future situations. They are particularly useful in such decision problems as technical diagnostics, performance evaluation and risk assessment. The situations are described by a set of attributes, which we might also call properties, features, characteristics, etc. Such attributes may be concerned with either the input or output of a situation. These situations may refer to states, examples, etc. Within this chapter, we will refer to them as objects. The goal of the chapter is to present a knowledge discovery paradigm for multi-attribute and multicriteria decision making, which is based upon the concept of rough sets. Rough set theory was introduced by (Pawlak 1982, Pawlak 1991). Since then, it has often proved to be an excellent mathematical tool for the analysis of a vague description of objects. The adjective vague (referring to the quality of information) is concerned with inconsistency or ambiguity. The rough set philosophy is based on the assumption that with every object of the universe U there is associated a certain amount of information (data, knowledge). This information can be expressed by means of a number of attributes. The attributes describe the object. Objects which have the same description are said to be indiscernible (similar) with respect to the available information.
    Original languageEnglish
    Title of host publicationSearch methodologies: introductory tutorials in optimization and decision support techniques
    EditorsE. Burke, G. Kendall
    Place of PublicationBerlin
    Number of pages53
    ISBN (Print)9780387234601
    Publication statusPublished - 2005


    Dive into the research topics of 'Rough set based decision support'. Together they form a unique fingerprint.

    Cite this