We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Integration of the Visible Color Spectrum and the Rough Sets Theory in the Process of Expert Knowledge Extraction

by Olga Pilipczuk
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 68 - Number 19
Year of Publication: 2013
Authors: Olga Pilipczuk
10.5120/11689-7387

Olga Pilipczuk . Integration of the Visible Color Spectrum and the Rough Sets Theory in the Process of Expert Knowledge Extraction. International Journal of Computer Applications. 68, 19 ( April 2013), 24-30. DOI=10.5120/11689-7387

@article{ 10.5120/11689-7387,
author = { Olga Pilipczuk },
title = { Integration of the Visible Color Spectrum and the Rough Sets Theory in the Process of Expert Knowledge Extraction },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 68 },
number = { 19 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 24-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume68/number19/11689-7387/ },
doi = { 10.5120/11689-7387 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:28:19.501672+05:30
%A Olga Pilipczuk
%T Integration of the Visible Color Spectrum and the Rough Sets Theory in the Process of Expert Knowledge Extraction
%J International Journal of Computer Applications
%@ 0975-8887
%V 68
%N 19
%P 24-30
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents the basic problems encountered in the process of expert knowledge extraction. Several experiments were carried out to compare the effectiveness of using linguistic and color data during this process. Particular attention was given to the color spectrum sets, and an example of how they can be applied in the rough sets theory is provided. The main purpose of the paper is to prove that the color spectrum scale more closely reflects people's opinions than the linguistic scale does and that it is possible to effectively integrate the color spectrum scale with the rough sets theory to extract expert knowledge. The experiments were conducted on a group of real estate specialists to obtain knowledge rules during the process of buying a building plot. Opinions were collected by questionnaire and interview. The experiments confirmed the hypothesis.

References
  1. Greco, S. , Matarazzo, B. , and S?owinski R. 1998. Rough approximation of a preference relation in a pair wise comparison table. In: Zopounidis S (Eds. ) Operational Tools in the Management of Financial Risks. Kluwer Academic Publishers, Dordrecht, 121-136.
  2. Huang, X. and Zhang, Y. 2003. A new application of rough set to ECG recognition, In Proceedings of Int. Conf. on Machine Learning and Cybernetics. 3, 1729–1734.
  3. Hvidsten, T. and Komorowski, J. 2007. Rough Sets in Bioinformatics, In: J. F. Peters et al. (Eds. ) Transactions on Rough Sets VII, Lecture Notes in Computer Science. 4400, 225–243.
  4. Mitra, P. , Mitra, S. , and Pal, S. 2001. Evolutionary modular MLP with rough sets and ID3 algorithm for staging of cervical cancer. Neural Computing and Applications. 10 (1), 67-76.
  5. Pedrycz, W. and Skowron, A. 2003. Rough sets, fuzzy sets in data mining, In: Handbook of Knowledge Discovery & Data Minining, W. Zytkow, W. Klosgen, (Eds. ). Oxford University Press.
  6. S?owi?ski, R. , and Zopounidis, C. 1994. Rough set sorting of rms according to bankruptcy risk, In: M. Paruccini (Eds. ) Applying multiple criteria aid for decision to environmental management. Kluwer Academic Publishers, Dordrecht, 339-357.
  7. S?owi?ski, R. 1995. Rough set approach to decision analysis, AI Expert, 10, 18-25.
  8. S?owi?ski, R. , Rough set theory and its applications to decision aid, Belgian Journal of Operation Research, 35 (3-4) , 81-90.
  9. Stokic, E. , Brtka, V. , and Srdic, B. 2010. The synthesis of the rough set model for the better applicability of sagittal abdominal diameter in identifying high risk patients. Computers in Biology and Medicine. 40, 786–790.
  10. Swiniarski, R. and Berzins, A. 1996. Rough Sets for Intelligent Data Mining, Knowledge discovering and Designing of an Expert Systems for on-line Prediction of Volleyball Game Progress. In: S. Tsumoto, S. Kobayashi, T. Yokomori, H. Tanaka, et al. (Eds. ) Proc. of the Fourth International Workshop on Rough Sets. Fuzzy Sets and Machine Discovery, 6-8, 413-418.
  11. Swiniarski, R. 1998. Texture recognition based on rough sets 2D FFT feature extraction. In Proceedings of World Automation Congress, Anchorage, Alaska.
  12. Tsumoto, S and Tanaka, H. 1996. Extraction of medical diagnostic knowledge based on rough set based model selection rule induction. Fuzzy Sets and Machine Discovery, 426-436.
  13. Tsumoto, S. 2004. Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model. Information Sciences: an International Journal, 162(2), 65–80.
  14. Hassanien, A. , Abraham, A. , Peters, J. , Member, S. , Peters, J. , and Schaefer, G. 2008. Overview of rough-hybrid approaches in image processing. In Proceedings of IEEE Conference on Fuzzy Systems, Hong Kong, 2135–2142.
  15. Lin, T. and Cercone, N. 1997. Rough sets and data mining. Kluwer, Dordrecht.
  16. Lin, T. and Liu, Q. 1994. Rough approximate operators: axiomatic rough set theory, In: Rough Sets, Fuzzy Sets and Knowledge Discovery, W. Ziarko (Eds. ) Springer-Verlag, London, 256-260.
  17. Mao, C. , Liu, S. , and Lin, J. 2004. Classification of multispectral images through a rough-fuzzy neural network. Optical Engineering. 43 (1),103-112.
  18. Petrosino, A. and Salvi, G. 2006. Rough fuzzy set based scale space transforms and their use in image analysis. International Journal of Approximate Reasoning. 41 (2), 212-228.
  19. Sarkar, M. 2002. Rough-fuzzy functions in classification. Fuzzy Sets and Systems, 132 (3), 353-369.
  20. Yao, Y. 1997. Combination of rough and fuzzy sets based on alpha-level sets, In: T. Lin, N. Cercone, Rough sets and data mining. Kluwer, Dordrecht, 301-321.
  21. Pain intensity scales. Product description. http://yhst-90616455459028. stores. yahoo. net/pain-intensity-scales-pocketsized. html
  22. Zappa, C. , Ho, D. , McGillis, W. , Banner, M. , Dacey, J. , et al. 2009. Rain-induced turbulence and air-sea gas transfer, Journal of geophysical research, 114.
  23. He, R. , Chang, G. , Wu H. , Lin, C. , Chiu, K. , Su, Y. , and Chen, S. 2006. Enhanced live cell membrane imaging using surface plasmon-enhanced total internal reflection fluorescence microscopy. Optical Express. 14 (20), 9307-9316.
  24. Yoshifuku, S. , Chen, Sh. , McMahon E. , et al. 2007, Parametric detection and measurement of perfusion defects in attenuated contrast echocardiographic images. Journal of ultrasound in medicine. 26 (6) 7, 39-48.
  25. Ubbelohde, N. , Fricke, Ch. , Flindt, Ch, et al. 2012. Measurement of finite-frequency current statistics in a single-electron transistor. Nature Communications. 3, 612.
  26. Breslow, L. A, Ratwani, R. M, and Trafton J. G. 2009. Cognitive models of the influence of color scale on data visualization tasks. Human Factors. Jun. 51(3), 321-38.
  27. Folker, S. , Sichelschmidt, L. and Ritter, H. 2005. Processing and Integrating Multimodal Material - The Influence of Color-Coding. In: B. G. Bara and L. Barsalou and M. Bucciarelli (Eds. ), Proceedings of the 27th Annual Conference of the Cognitive Science Society, 690-695.
  28. Hyun, Y. 2008. Nonlinear Color Scales for Interactive Exploration. http://www. caida. org/ ˜youngh/colorscales/nonlinear. html.
  29. Schulze-Wollgast P. , Tominski, Ch. , Schumann, H. 2005. Enhancing Visual Exploration by Appropriate Color Coding. In WSCG (Full Papers), 203-210.
  30. Pawlak, Z. and S?owi?ski, R. 1994. Decision analysis using rough sets. International Transactions in Operational Research. 1 (1), 107-114.
  31. Polkowski, L. 2003. Rough Sets. Mathematical Foundations. Physica-Verlag Heidelberg.
  32. Azevedo, P. and Jorge, A. M. 2007. Comparing rule Measures for Predictive Association Rules. In Proceedings of ECML. Springer-Verlag. Berlin Heidelberg, 510-517.
  33. Lenca, Ph. , Meyer, P. , Vaillant, B. , and Lallich, S. 2008. On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid, European Journal of Operational Research. 184 (2), 610-626.
  34. Yao Y. and Zhong, N. 1999. An analysis of quantitative measures associated with rules. Lecture Notes in Artificial Intelligence, Springer-Verlag, Berlin, 479-488.
  35. S?owi?ski, R. and Greco, S. 2005. Measuring attractiveness of rules from the viewpoint of knowledge representation. Lecture Notes in Computer Science. 3528, 950-953.
Index Terms

Computer Science
Information Sciences

Keywords

Rough Sets Color Coding Knowledge Extraction

Powered by PhDFocusTM