IEEE Style Citation: S.V.S.G. Devi, “Prediction Of A Class Variable In Classification Problem Using Fuzzy Inference Method,” International Journal of Computer Sciences and Engineering, Vol.2, Issue.1, pp.28-29, 2014.
MLA Style Citation: S.V.S.G. Devi "Prediction Of A Class Variable In Classification Problem Using Fuzzy Inference Method." International Journal of Computer Sciences and Engineering 2.1 (2014): 28-29.
APA Style Citation: S.V.S.G. Devi, (2014). Prediction Of A Class Variable In Classification Problem Using Fuzzy Inference Method. International Journal of Computer Sciences and Engineering, 2(1), 28-29.
BibTex Style Citation:
@article{Devi_2014,
author = {S.V.S.G. Devi},
title = {Prediction Of A Class Variable In Classification Problem Using Fuzzy Inference Method},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2014},
volume = {2},
Issue = {1},
month = {1},
year = {2014},
issn = {2347-2693},
pages = {28-29},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=35},
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=35
TI - Prediction Of A Class Variable In Classification Problem Using Fuzzy Inference Method
T2 - International Journal of Computer Sciences and Engineering
AU - S.V.S.G. Devi
PY - 2014
DA - 2014/02/04
PB - IJCSE, Indore, INDIA
SP - 28-29
IS - 1
VL - 2
SN - 2347-2693
ER -
VIEWS
PDF
XML
4112
3903 downloads
3859 downloads
Abstract
A popular and particularly efficient method for making a decision tree for classification from symbolic data is ID3 algorithm. Revised algorithms for numerical data have been proposed, some of which divide a numerical range into several intervals or fuzzy intervals. Their decision trees, however, are not easy to understand. A new version of ID3 algorithm to generate a understandable fuzzy decision tree using fuzzy sets defined by a user. In this paper, first the fuzzy decision tree is constructed for the given data and then fuzzy reasoning is applied in order to predict the class variable.
Key-Words / Index Term
Fuzzy Technique
References
[1] J.R. Quinlan (1979}: �Discovering Rules by Induction from large collections of Examples�, in d.Michie (ed.): Expert Systems in the Micro Electronics Age, Edinburgh University Press.
[2] J.R. Quinlan (1986): �Induction of Decision Trees�, Machine Learning, Vol.1, pp.81-106.
[3] T. Tani and M. Sakoda (1991): �Fuzzy Oriented Expert System to Determine Heater Outlet Temperature Applying Machine Learning�, 7th Fuzzy System Symposium (Japan Society for Fuzzy Theory and Systems), pp.659-662 (in Japanese).
[4] S. Sakurai and D. Araki (1992): �Application of Fuzzy Theory to Knowledge Acquisition�, 15th Intelligent System Symposium (Society of Instrument and Control Engineers), pp.169-174 (in Japanese).
[5] H. Ichihashi (1993): �Tuning Fuzzy Rules by Neuro-Like Approach�, Journal of Japan Society for Fuzzy Theory and Systems, Vol.5, No.2, pp.191-203 (in Japanese).
[6] F. Kawachi and T. matsuura (1990): �Development of Expert System for Diagnosis by Gas in Oil and Its Evaluation in Practice Usage�, Technical Meeting on electrical Insulation Material (The Institute of Electrical Engineers of Japan), EIM-90-40 (In Japanese).
VIEWS
PDF 
XML 
