A MODIFIED CASE-BASED REASONING METHOD BASED ON THE ROUGH SET THEORY

Authors

  • I. I. Kovalenko Petro Mohyla Black Sea National University, Mykolayiv, Ukraine., Ukraine
  • A. V. Shved Petro Mohyla Black Sea National University, Mykolayiv, Ukraine., Ukraine
  • N. V. Koval Petro Mohyla Black Sea National University, Mykolayiv, Ukraine., Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2018-4-10

Keywords:

Rough Set Theory, Case-Based Reasoning, case (precedent), knowledge base, case base, classification.

Abstract

Context. Knowledge bases are the main element of artificial intelligence systems. They are formed on the basis of two generally accepted approaches: the object-oriented approach and object-structural approach. Knowledge structuring through its ordering,
classification and typing of selected classes is the main operation that is implemented in both approaches. Quite often there are situations when data or knowledge is not exact and it is impossible to perform their exact classification. These features necessitate the
development of new approaches aimed at solving problems of extracting knowledge from large arrays of unordered data, structuring, presenting and analytical processing of inexact knowledge in automated construction of knowledge bases.
Objective. The objective of this paper is a research of new approaches for solving problems of representation of knowledge about cases in intellectual decision support systems.
Method. An approach aimed at modifying Case-Based Reasoning method on the basis of Rough Set Approach has been proposed in this paper. The proposed method forms a partition of cases to determine the degree of their belonging to the goal classes
using upper and lower approximations of goal classes, considering the relative importance of classification attributes and formed equivalence classes.
Results. The proposed modification of Case-Based Reasoning method allows extracting knowledge about cases from arrays of unordered data with the purpose of the case base construction, and handling the inconsistent (in cases where for the same values of
attributes cases belong to different classes), and incomplete (in cases where the values of some attributes or information of the case belonging to the given class is missing or unreliable) information about cases.
Conclusions. The proposed method of representation knowledge about cases, their adaptation and subsequent search in the case base formed under uncertainty and existence of inexact, rough, inconsistent initial data constitutes a theoretical basis for constructing
intellectual decision support systems. 

References

Varshavskij P. R., Eremeev A. P. Modelirovanie

rassuzhdenij na osnove precedentov v intellektual’nyx

sistemax podderzhki prinyatiya reshenij, Iskusstvennyj

intellekt i prinyatie reshenij, 2009, No. 2, pp. 45–57.

Varshavskij P. R., Eremeev A. P. Metody pravdopodobnyx

rassuzhdenij na osnove analogij i precedentov dlya

intellektual’nyx sistem podderzhki prinyatiya reshenij,

Novosti iskusstvennogo intellekta, 2006, No. 3, pp. 39–62.

Eremenko T. K., Pilipenko Yu. G. Ispol’zovanie CBRpodxoda

dlya baz znanij situacionnyx centrov, Systemy

pіdtrymky pryiniattia rіshen. Teorіia і praktyka, 2010,

pp. 151–153.

Klymchuk S. A. Primenenie precedentov dlya diagnostiki

kranov mostovogo tipa, Systemni doslidzhennia ta

informatsiini tekhnolohii, 2012, No. 4, pp. 17–22.

Ulshyn V. O., Klymchuk S. O. Modelі predstavlennia znan

ekspertnykh system pіdtrymky pryiniattia rіshen pry

dіagnostuvannі / V. O. Ulshyn, // Vіsnyk SNU іm. V.

Dalia. – 2009. – Vol. 135, № 5. – S. 21–25.

Nefedov L. I., Fil’ N. Yu., Gubin Yu. L. Metod poiska

precedentov proektov likvidacii chrezvychajnyx prirodnyx

situacij na magistral’nyx avtomobil’nyx dorogax,

Vostochno-Evropejskij zhurnal peredovyx texnologij, 2010,

No. 1, pp. 50–52.

Haritonov Yu. N. Upravlenie proektami rekonstrukcii na

osnove artefaktnyx platform, Aviacionno-kosmicheskaya

texnika i texnologiya, 2008, No. 8, pp. 189–192.

Pawlak Z. Rough Sets Theoretical Aspects of Reasoning

about Data. Boston/London, Academic Publishers, 1991,

p.

Uzhga-Rebrov O. Osobennosti predstavleniya znanij v teorii

grubyx mnozhestv, Environment. Technology. Resources:

th International Scientific and Practical Conference,

Rezekne, 25–27 June, 2009: proceedings. Latvia,

Izdevnieciba, 2009, Vol. 2, P. 169–176.

Uzhga-Rebrov O. I. Upravlenie neopredelennostyami.

Sovremennye neveroyatnostnye metody. Latvia, RA

Izdevnieciba, 2010, Vol. 3, 560 p.

How to Cite

Kovalenko, I. I., Shved, A. V., & Koval, N. V. (2019). A MODIFIED CASE-BASED REASONING METHOD BASED ON THE ROUGH SET THEORY. Radio Electronics, Computer Science, Control, (4). https://doi.org/10.15588/1607-3274-2018-4-10

Issue

Section

Neuroinformatics and intelligent systems