PL EN


2014 | 1(35) | 133-140
Article title

Extracting Decision Rules from Linguistic Data Describing Economic Phenomena. The Approach Based on Decision Systems over Ontological Graphs and PSO

Title variants
PL
Ekstrakcja reguł decyzyjnych z danych lingwistycznych opisujących zjawiska ekonomiczne. Podejście oparte na systemach decyzyjnych nad grafami ontologicznymi oraz optymalizacji rojem cząstek
Languages of publication
EN
Abstracts
EN
The aim of the paper is to present a heuristic method for extracting the most general decision rules from linguistic data describing economic phenomena included in simple decision systems over ontological graphs. Such decision systems have been proposed to deal with linguistic attribute values, describing objects of interest, which are concepts placed in semantic spaces expressed by means of ontological graphs. Ontological graphs deliver some additional knowledge (the so-called background knowledge) about semantic relations between concepts which can be useful in classification processes. As heuristics, we propose to use Particle Swarm Optimization (PSO ), which is reported as a successful method in many applications.
PL
Celem artykułu jest przedstawienie heurystycznej metody ekstrakcji najbardziej ogólnych reguł decyzyjnych z danych lingwistycznych, opisujących zjawiska ekonomiczne, zawartych w systemach decyzyjnych nad grafami ontologicznymi. Systemy decyzyjne tego typu zaproponowane zostały w celu poradzenia sobie z lingwistycznymi wartościami atrybutów opisującymi rozważane obiekty, które są pojęciami umieszczonymi w przestrzeniach semantycznych reprezentowanych przez grafy ontologiczne. Grafy ontologiczne dostarczają nam pewną dodatkową wiedzę (tzw. wiedzę bazową) o relacjach semantycznych pomiędzy pojęciami, która może być pomocna w procesach klasyfikacji. Jako heurystykę zaproponowano optymalizację rojem cząstek (Particle Swarm Optimization ) uważaną za metodę odnoszącą sukces w wielu zastosowaniach.
Contributors
  • University of Management and Administration in Zamość
  • University of Information Technology and Management in Rzeszów
  • University of Information Technology and Management in Rzeszów
References
  • Chaffin, R., and D.J. Hermann. 1988. “The Nature of Semantic Relations. A Comparison of Two Approaches.” In Relational Models of the Lexicon. Representing Knowledge in Semantic Networks, edited by M.W. Evens, 289–334. Cambridge England; New York: Cambridge University Press.
  • Cios, K.J., W. Pedrycz, R.W. Swiniarski, and L. Kurgan. 2007. Data Mining. A Knowledge Discovery Approach. New York: Springer.
  • Greco, S., B. Matarazzo, and R. Slowiński. 2001. “Rough Sets Theory for Multicriteria Decision Analysis.” European Journal of Operational Research no. 129 (1): 1–47. doi: 10.1016/S0377–2217 (00)00167–3.
  • Han, J.W., Y.D. Cai, and N. Cercone. 1992. “Knowledge Discovery in Databases — an Attribute-Oriented Approach.” Very Large Data Bases: VLDB – 92:547–559.
  • Ishizu, S., A. Gehrmann, Y. Nagai, and Y. Inukai. 2007. “Rough Ontology. Extension of Ontologies by Rough Sets.” Human Interface and the Management of Information: Methods, Techniques and Tools in Information Design, LNCS Proceedings no. 4557:456–462.
  • Kohler, J., S. Philippi, M. Specht, and A. Ruegg. 2006. “Ontology Based Text Indexing and Querying for the Semantic Web.” Knowledge-Based Systems no. 19 (8): 744–754. doi: 10.1016/j.knosys.2006.04.015.
  • Kudoh, Y., M. Haraguchi, and Y. Okubo. 2003. “Data Abstractions for Decision Tree Induction.” Theoretical Computer Science no. 292 (2): 387–416. doi: 10.1016/S0304–3975 (02)00178–0.
  • Lukaszewski, T., J. Józefowska, and A. Lawrynowicz. 2012. “Attribute Value Ontology — Using Semantics in Data Mining.” In ICEIS 2012 — Proceedings of the 14th International Conference on Enterprise Information Systems, Volume 2, Wroclaw, Poland, 28 June – 1 July, 2012, edited by L.A. Maciaszek, A. Cuzzocrea and J. Cordeiro, 329–334. Wrocław, Poland: SciTePress.
  • Łukaszewski, T., J. Józefowska, A. Ławrynowicz, and Ł. Józefowski. 2011. “Handling the Description Noise Using an Attribute Value Ontology.” Control and Cybernetics no. 40 (2): 275–292.
  • Midelfart, H., and J. Komorowski. 2002. “A Rough Set Framework for Learning in a Directed Acyclic Graph.” Rough Sets and Current Trends in Computing, Proceedings no. 2475:144–155.
  • Milstead, J.L. 2001. “Standards for Relationships Between Subject Indexing Terms.” In Relationships in the Organization of Knowledge, edited by C.A. Bean and R. Green, 53–66. Dordrecht-Boston-Norwell, MA: Kluwer Academic Publishers.
  • Mitchell, T.M. 1982. “Generalization as Search.” Artificial Intelligence no. 18 (2): 203–226. doi: 10.1016/0004–3702 (82)90040–6.
  • Mitchell, T.M. 1997. Machine Learning, McGraw-Hill series in computer science. New York: McGraw-Hill.
  • Neches, R., R. Fikes, T. Finin, T. Gruber, R. Patil, T. Senator, and W.R. Swartout. 1991. “Enabling Technology for Knowledge Sharing.” AI Magazine no. 12 (3): 36–56.
  • Nunez, M. 1991. “The Use of Background Knowledge in Decision Tree Induction.” Machine Learning no. 6 (3): 231–250. doi: 10.1007/Bf00114778.
  • Pancerz, K. 2012a. “Dominance-Based Rough Set Approach for Decision Systems over Ontological Graphs.” In Proceedings of the Federated Conference on Computer Science and Information Systems, September 9–12, 2011. Wrocław, Poland, edited by M. Ganzha, L. Maciaszek and P. M., 323–330. Wrocław: Polskie Towarzystwo Informatyczne; IEEE Computer Society Press.
  • Pancerz, K. 2012b. “Toward Information Systems over Ontological Graphs.” In Rough Sets and Current Trends in Computing, edited by J. Yao, Y. Yang, R. Słowiński, S. Greco, H. Li, S. Mitra and L. Polkowski, 243–248. Berlin: Springer Berlin Heidelberg.
  • Pancerz, K. 2013a. “Decision Rules in Simple Decision Systems over Ontological Graphs.” In Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013, edited by R. Burduk, K. Jackowski, M. Kurzyński, M. Woźniak and A. Żołnierek, 111–120. Cham-Heidelberg-New York-Dordrecht-London: Springer International Publishing.
  • Pancerz, K. 2013b. “Semantic Relationships and Approximations of Sets: An Ontological Graph Based Approach.” In 6th International Conference on Human System Interactions (HSI). June 06–08, 2013. Gdańsk, Sopot, Poland, edited by W.A. Paja and B.M. Wilamowski, 62–69. Gdańsk: IEEE.
  • Pawlak, Z. 1991. Rough Sets. Theoretical Aspects of Reasoning about Data, Theory and decision library Series D, System theory, knowledge engineering, and problem solving. Dordrecht-Boston: Kluwer Academic Publishers.
  • Poli, R. 2008. “Analysis of the Publications on the Applications of Particle Swarm Optimisation.” Journal of Artificial Evolution and Applications no. 2008:1–10. doi: 10.1155/2008/685175.
  • Poli, R., J. Kennedy, and T. Blackwell. 2007. “Particle Swarm Optimization.” Swarm Intelligence no. 1 (1): 33–57. doi: 1007/s11721–007–0002–0.
  • Srikant, R., and R. Agrawal. 1997. “Mining Generalized Association Rules.” Future Generation Computer Systems no. 13 (2–3): 161–180. doi: 10.1016/S0167–739x (97)00019–8.
  • Storey, V.C. 1993. “Understanding Semantic Relationships.” The International Journal on Very Large Data Bases no. 2 (4): 455–488. doi: 10.1007/BF01263048.
  • Vapnik, V.N. 1998. Statistical Learning Theory, Adaptive and Learning Systems for Signal Processing, Communications, and Control. New York: Wiley.
  • Winston, M.E., R. Chaffin, and D. Herrmann. 1987. “A Taxonomy of Part-Whole Relations.” Cognitive Science no. 11 (4): 417–444. doi: 10.1207/s15516709cog1104_2.
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.desklight-32e555b4-67a8-4834-bc71-aad103d37b4f
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