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 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 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

Real Life Applications of Fuzzy Decision Tree

by Kavita Sachdeva, Madasu Hanmandlu, Amioy Kumar
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 10
Year of Publication: 2012
Authors: Kavita Sachdeva, Madasu Hanmandlu, Amioy Kumar
10.5120/5730-7800

Kavita Sachdeva, Madasu Hanmandlu, Amioy Kumar . Real Life Applications of Fuzzy Decision Tree. International Journal of Computer Applications. 42, 10 ( March 2012), 24-28. DOI=10.5120/5730-7800

@article{ 10.5120/5730-7800,
author = { Kavita Sachdeva, Madasu Hanmandlu, Amioy Kumar },
title = { Real Life Applications of Fuzzy Decision Tree },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 10 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 24-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number10/5730-7800/ },
doi = { 10.5120/5730-7800 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:30:59.726991+05:30
%A Kavita Sachdeva
%A Madasu Hanmandlu
%A Amioy Kumar
%T Real Life Applications of Fuzzy Decision Tree
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 10
%P 24-28
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Fuzzy Decision Tree is becoming increasingly significant as it is applied to areas of different platforms in real life. This paper gives an overview of the applications of fuzzy decision tree in heterogeneous fields. It is being actively used in fields as varied as intrusion detection, flexible querying (modus ponens), analysis of cognitive process (Human Computer Interaction), for user authentication in biometrics, as parallel processing support, in stock-market, for information retrieval and data mining. The fuzzy logic approach allows the direct incorporation of expertise. Moreover, it is shown that the decision taken by means of a fuzzy decision tree is more stable when observation evolves. The powerful combinatorial methods found in fuzzy logic have been used to prove fundamental results in other areas namely medical, educational, chemical and multimedia.

References
  1. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and Regression Trees. Wadsworth: Belmont, CA, 1984.
  2. Quinlan JR. C4. 5 Programs for Machine Learning. Morgan Kaufmann: San Mateo, CA, 1993.
  3. Yuan Y, Shaw MJ. Induction of fuzzy decision trees. Fuzzy Sets Syst. 1995; 69: 125–139.
  4. Suarez A, Lutsko JF. Globally optimal fuzzy decision trees for classification and regression. IEEE Trans. Pattern Anal. Machine Intell. 1999; 21: 1297–1311.
  5. Zadeh L. : Fuzzy sets, Information and control, 8, pp. 407–428, 1965.
  6. L. A. Zadeh, "Fuzzy sets," Inform. Control, 338-353, 1965.
  7. H. J. Zimmermann, Fuzzy Set Theory and Its Applications, Kluwer Academic Publishers, 1991.
  8. Jang, J. S. R. , Self-Learning Fuzzy Controllers Based on Temporal Back-Propagation, IEEE Trans. On Neural Network, Vol. 3, September, 1992, pp. 714-723.
  9. Horikowa, S. , T. Furahashi and Y. Uchikawa, On Fuzzy Modeling Using Fuzzy Neural Networks with Back-Propagation Algorithm, IEEE Trans. on Neural Networks, Vol. 3, Sept. , 1992, pp. 801-806.
  10. B. Bouchon-Meunier and C. Marsala: Learning fuzzy decision rules. Handbook of Fuzzy Sets, Kluwer, Dordrecht 1999.
  11. I-M. A. Chen: Query answering using discovered rules. In: Proc 12th Internat. Conference on Data Engineering, 1996, pp. 402-411.
  12. S. Shekhar, B. Hamidzadeh, A. Kohli, and M. Coyle: Learning transformation rules for semantic query optimization: A data-driven approach. IEEE Trans, on Knowledge and Data Engineering 5 (1993), 6, 950-964.
  13. Witold Pedrycz, Fellow, IEEE, and Zenon A. Sosnowski, "C–Fuzzy Decision Trees",IEEE Transactions on Systems, Man, and Cybernetics —Part C: Applications and Reviews, VOL. 35, NO. 4,November 2005.
  14. Huan Liu, Lei Yu , " Toward Integrating Feature selection Algorithms for Classification and Clustering", IEEE Transaction on Knowledge and data Engineering VOl 17 , No 4, April 2005.
  15. Makkithaya, K. , Subba Reddy, N. V. , Dinesh Acharya, U. "Improved C-Fuzzy Decision Tree for Intrusion Detection", World Academy of Science, Engineering and Technology, vol. 32, 2008.
  16. U. M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth: From data mining to knowledge discovery in databases. AI Magazine 17 (1996), 3, 37-54.
  17. C. Marsala and B. Bouchon-Meunier: An adaptable system to construct fuzzy decision trees. In: Proc. NAFIPS'99 (North American Fuzzy Information Processing Society), New York 1999, pp. 223-227.
  18. I-M. A. Chen: Query answering using discovered rules. In: Proc 12th Internat. Conference on Data Engineering (S. Y. W. Su, ed. ),New Orleans 1996, pp. 402-411.
  19. S. Shekhar, B. Hamidzadeh, A. Kohli, and M. Coyle: Learning transformation rules for semantic query optimization: A data-driven approach. IEEE Trans, on Knowledge and Data Engineering 5 (1993), 6, 950-964.
  20. Christophe Marsala. Fuzzy decision trees to help flexible querying. Kybernetika, Vol. 36 (2000), No. 6, 689-705.
  21. Damez, M. , Marsala, C. , Dang, T. , & Bouchon-Meunier, B. (2005). Fuzzy decision tree for user modeling from human–computer interactions. In International conference on human system learning. (pp. 287–302)
  22. Karthik Nandakumar, Integration Of Multiple Cues In Biometric System,M. S. Thesis, http://www. cse. msu. edu/biometrics/Publications/Thesis. 2005
  23. ID3 algorithm. Wikipedia, the free encyclopedia, http://en. wikipedia. org/wiki/ID3algorithm. (May2001)
  24. ID3 algorithm. Wikipedia,the free encyclopedia, http://en. wikipedia. org/wiki/pruning. (May 2011)
  25. Quinlan J. R. : Induction of Decision Trees, Machine Learning, 1, pp. 81-106, 1986.
  26. V. Levashenko, and P. Martincova?, "Fuzzy Decision Tree for Parallel Processing Support", Journal of Information, Control and Management Systems, vol. 3, 1, 2005, pp. 45-52.
  27. Mohd Noor Md Sap, R. H. Khokhar. Fuzzy Decision Tree for Data Mining of Time Series Stock Market Databases, The fifth International conference for the Critical Assessment of Microarray Data Analysis 2004,USA.
  28. B. Bouchon-Meunier, M. Detyniecki, M. -J. Lesot, C. Marsala, M. Rifqi. "Real world fuzzy logic applications in data mining and information retrieval". Springer, pp. 219-247, 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Biometric Classification Data Mining Fuzzy Decision Tree Fuzzy Logic Intrusion Detection Parallel Processing Tafpa Hci

Powered by PhDFocusTM