DOI QR코드

DOI QR Code

Ontology-based Fuzzy Classifier for Pattern Classification

패턴분류를 위한 온톨로지 기반 퍼지 분류기

  • Lee, In-K. (Dept. of Electrical Engineering, Yeungnam University) ;
  • Son, Chang-S. (Dept. of Electrical Engineering, Yeungnam University) ;
  • Kwon, Soon-H. (Dept. of Electrical Engineering, Yeungnam University)
  • 이인근 (영남대학교 전기공학과) ;
  • 손창식 (영남대학교 전기공학과) ;
  • 권순학 (영남대학교 전기공학과)
  • Published : 2008.12.25

Abstract

Recently, researches on ontology-based pattern classification have been tried out in many fields. However, in most of the researches, the ontology which represents the knowledge about pattern classification is just referred during the processes of the pattern classification. In this paper, we propose ontology-based fuzzy classifier for pattern classification which is extended from the fuzzy rule-based classifier In order to realize the proposed classifier, we construct an ontology by conceptualizing the method of fuzzy rule-based pattern classification and generate ontology inference rules for pattern classification. Lastly, we show the validity o) the proposed classifier through the experiment of pattern classification on the Fisher's IRIS dataset.

References

  1. N.E. Maillot and M. Thonnat, 'Ontology based complex object recognition,' Image and Vision Computing, Vol. 26, pp. 102-113, 2008 https://doi.org/10.1016/j.imavis.2005.07.027
  2. V. Mezaris, I. Kompatsiaris, and M.G. Strintzis, 'Region-based Image Retrieval Using an Object Ontology and Relevance Feedback,' Eurasip Journal on Applied Signal Processing, Vol. 6, pp. 886-901, 2004
  3. M.H. Wang, C.S. Lee, H.C. Li, and W.M. Ko, 'Ontology-based fuzzy inference agent for diabetes classification,' North American Fuzzy Information Processing Society (NAFIPS), pp. 79-83, 2007
  4. K. Wolstencroft, P. Lord, and L. Tabernero, 'Protein classification using ontology classification,' Bioinformatics, Vol. 22, No. 14, pp. e530-e538, 2006 https://doi.org/10.1093/bioinformatics/btl208
  5. R.E. Wray, S.A. Lisse, and J.T. Beard, 'Ontology infrastructure for execution-oriented autonomous agents,' Robotics and Autonomous Systems, Vol. 49, pp. 113-122, 2004 https://doi.org/10.1016/j.robot.2004.07.019
  6. R. Provine, C. Schlenoff, S. Balakirsky, S. Smith, M. Uschold, 'Ontology-based methods for enhancing autonomous vehicle path planning,' Robotics and Autonomous Systems, Vol. 49, pp. 123-133, 2004 https://doi.org/10.1016/j.robot.2004.07.020
  7. C.S. Lee, and M.H. Wang, 'Ontology-based intelligent healthcare agent and its application to respiratory waveform recognition,' Expert Systems with Applications, Vol. 33, No. 3, pp. 606-619, 2007 https://doi.org/10.1016/j.eswa.2006.06.006
  8. J. Casillas, O. Cordon, F. Herrera, and M.J. Del Jesus, 'Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems,' Information sciences, Vol. 136, pp. 135-157, 2001 https://doi.org/10.1016/S0020-0255(01)00147-5
  9. V. Uebele, S. Abe, and M.S. Lan, 'A neural network based fuzzy classifier,' IEEE Transactions on SMC, Vol. 25, pp. 353-361, 1995 https://doi.org/10.1109/21.364829
  10. Y.C. Tsai, C.H. Cheng, and J.R. Chang, 'Entropy-based fuzzy rough classification approach for extracting classification rules,' Experts systems with applications, Vol. 31, No. 2, pp. 436-443, 2006 https://doi.org/10.1016/j.eswa.2005.09.038
  11. 손창식, 정환묵, 권순학, '퍼지 규칙기반 분류시스템에서 퍼지 분할의 선택방법,' 지능시스템학회 논문지, 제18권, 3호, pp. 360-366, 2008 https://doi.org/10.5391/JKIIS.2008.18.3.360
  12. R.A. Fisher, 'The Use of Multiple Measurements in Taxonomic Problems,' In Annual Eugenics, Vol. 7, No. 2, pp. 179-188, 1936 https://doi.org/10.1111/j.1469-1809.1936.tb02137.x
  13. OWL Web Ontology Language Guide, http://www.w3.org/TR/owl-guide/
  14. XML Schema, http://www.w3.org/XML/Schema
  15. Jena-A Semantic Web Framework for Java, http://jena.sourceforge.net/
  16. SPARQL Query Language for RDF, http://www.w3.org/TR/rdf-sparql-query/