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Modelling and Reasoning with Paraconsistent Rough Sets
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
Coll Econ and Comp Science, PL-10061 Olsztyn, Poland .
Warsaw University, Institute Informat, PL-02097 Warsaw, Poland .
2009 (English)In: Fundamenta Informaticae, ISSN 0169-2968, Vol. 97, no 4, 405-438 p.Article in journal (Refereed) Published
Abstract [en]

We present a language for defining paraconsistent rough sets and reasoning about them. Our framework relates and brings together two major fields: rough sets [23] and paraconsistent logic programming [9]. To model inconsistent and incomplete information we use a four-valued logic. The language discussed in this paper is based on ideas of our previous work [21, 32, 22] developing a four-valued framework for rough sets. In this approach membership function, set containment and set operations are four-valued, where logical values are t (true), f (false), i (inconsistent) and u (unknown). We investigate properties of paraconsistent rough sets as well as develop a paraconsistent rule language, providing basic computational machinery for our approach.

Place, publisher, year, edition, pages
2009. Vol. 97, no 4, 405-438 p.
Keyword [en]
approximate reasoning, rough sets, paraconsistent reasoning, four-valued logics
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-53059DOI: 10.3233/FI-2009-209OAI: diva2:286646
Available from: 2010-01-15 Created: 2010-01-15 Last updated: 2010-12-06
In thesis
1. Reasoning with Rough Sets and Paraconsistent Rough Sets
Open this publication in new window or tab >>Reasoning with Rough Sets and Paraconsistent Rough Sets
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis presents an approach to knowledge representation combining rough sets and para-consistent logic programming.

The rough sets framework proposes a method to handle a specific type of uncertainty originating from the fact that an agent may perceive different objects of the universe as being similar, although they may have di®erent properties. A rough set is then defined by approximations taking into account the similarity between objects. The number of applications and the clear mathematical foundation of rough sets techniques demonstrate their importance.

Most of the research in the rough sets field overlooks three important aspects. Firstly, there are no established techniques for defining rough concepts (sets) in terms of other rough concepts and for reasoning about them. Secondly, there are no systematic methods for integration of domain and expert knowledge into the definition of rough concepts. Thirdly, some additional forms of uncertainty are not considered: it is assumed that knowledge about similarities between objects is precise, while in reality it may be incomplete and contradictory; and, for some objects there may be no evidence about whether they belong to a certain concept.

The thesis addresses these problems using the ideas of paraconsistent logic programming, a recognized technique which makes it possible to represent inconsistent knowledge and to reason about it. This work consists of two parts, each of which proposes a di®erent language. Both languages cater for the definition of rough sets by combining lower and upper approximations and boundaries of other rough sets. Both frameworks take into account that membership of an object into a concept may be unknown.

The fundamental difference between the languages is in the treatment of similarity relations. The first language assumes that similarities between objects are represented by equivalence relations induced from objects with similar descriptions in terms of a given number of attributes. The second language allows the user to define similarity relations suitable for the application in mind and takes into account that similarity between objects may be imprecise. Thus, four-valued similarity relations are used to model indiscernibility between objects, which give rise to rough sets with four-valued approximations, called paraconsistent rough sets. The semantics of both languages borrows ideas and techniques used in paraconsistent logic programming. Therefore, a distinctive feature of our work is that it brings together two major fields, rough sets and paraconsistent logic programming.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. 43 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1307
National Category
Engineering and Technology
urn:nbn:se:liu:diva-60794 (URN)978-91-7393-411-4 (ISBN)
Public defence
2010-11-19, K3, Kåkenhus, Campus Norrköping, Linköpings universitet, Norrköping, 09:15
Available from: 2010-10-26 Created: 2010-10-26 Last updated: 2016-06-10Bibliographically approved

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