Open Access Open Access  Restricted Access Subscription Access

An Algorithm To Building A Fuzzy Decision Tree For Data Classification Problem Based On The Fuzziness Intervals Matching

Lê Văn Tường Lân, Nguyễn Mậu Hân, Nguyễn Công Hào

Abstract


Nowaday, on demand to reflect the real world, so we have many imprecise stored business data warehouses. The precise data classification can not solve all the requirements. Thus, fuzy decision tree classification problem have role is important of fuzzy data mining problem.

The fuzzy decision classification based on fuzzy set theory have some limitations derived from the inner selves of it. The hedge algebra with many advantages has become a really useful tool for solving the fuzzy decision tree classification.

However, sample data homogeniseprocess based on quantitative methods ofthe hedge algebra withsome restrictions remain appear because of error in the process and not the result tree truly versatile. So,the fuzzy decision tree obtained not always have high predictable. In this paper, weusing  fuzzinessintervals matching an approachhedge algebra, we proposedthe inductive learning method HAC4.5 fuzzy decision tree to obtain the fuzzy decision tree with high predictable.


Keywords


Hedge algebra, liguistic, homogenise, fuzzy decision tree, HAC4.5.

References


. Duong Thang Long: Method to built fuzzy rule system based on hedge algebra semantic and applied for classification problem, IOIT, 2010.

. Nguyen Cong Hao: Fuzzy databases with data manipulation based on hedge algebra, Thesis of Doctor mathematic, IOIT, 2008.

. Nguyen Cat Ho, Tran Thai Son: On distance between linguistic values in hedge algebra, Journal of Computer Science and Cybernetics, Vol 11(1), pp. 10-20, 1995.

. Le Xuan Viet: SemanticQuantitativelinguistic values of linguistic variable inhedge algebra and applied, Thesis of Doctor mathematic, IOIT, 2009.

. A.K. Bikas, E. M. Voumvoulakis and N. D. Hatziargyriou: Neuro-Fuzzy Decision Trees for Dynamic Security Control of Power Systems, Department of Electrical and Computer Engineering, NTUA, Athens, Greece, 2008.

. Abonyi J., Roubos J.A. and Setnes M.: Learning fuzzy classification rules from labeled data, Information Sciences, vol.150, 2003.

. B. Chandra: Fuzzy SLIQ Decision Tree Algorithm, IEEE, 2008.

. Chang, Robin L. P. Pavlidis, Theodosios: Fuzzy Decision Tree Algorithms, Man and Cybernetics, IEEE , 2007.

. Fuller R.: Neural Fuzzy Systems, Physica-Verlag, Germany, 1995.

. Hesham A. Hefny, Ahmed S. Ghiduk, Ashraf Abdel Wahab: Effective Method for Extracting Rules from Fuzzy Decision Trees based on Ambiguity and Classifiability, Universal Journal of Computer Science and Engineering Technology, Cairo University, Egypt., pp. 55-63, Oct. 2010.

. Ho N. C. and Long N. V.: Fuzziness measure on complete hedges algebras and quantifying semantics of terms in linear hedge algebras, Fuzzy Sets and Systems, vol.158, pp.452-471, 2007.

. Ho N. C. and Nam H. V.: An algebraic approach to linguistic hedges in Zadeh's fuzzy logic, Fuzzy Sets and Systems, vol.129, pp.229-254, 2002.

. Ho N. C. and Wechler W.: Hedge algebras: an algebraic approach to structures of sets of linguistic domains of linguistic truth variables, Fuzzy Sets and Systems, 35(3), pp. 281-293, 1990.

. Ho N. C. and Wechler W.: Extended algebra and their application to fuzzy logic, Fuzzy Sets and Systems, vol.52, pp. 259–281, 1992.

. Ishibuchi H. and Nakashima T.: Effect of Rule Weights in Fuzzy Rule-Based Classification Systems, IEEE Trans. on Fuzzy Systems, vol.9, no.4, 2001.

. James F. Smith III, ThanhVu H. Nguyen: Genetic program based data mining of fuzzy decision trees and methods of improving convergence and reducing bloat, Data Mining, Intrusion Detection, Information Assurance, 2007

. Lan L.V.T., Han N.M., Hao N.C.: A Novel Method to Build a Fuzzy Decision Tree Based On Hedge Algebras,International Journal of Research in Engineering and Science, Volume 4, Issue 4, pp.16-24, 2016

. Lee C.S. George and Lin C.T: Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, Prentice-Hall International, Inc, 1995.

. Moustakidis, S. Mallinis, G. Koutsias, N. Theocharis, J.B. Petridis: SVM-Based Fuzzy Decision Trees for Classification of High Spatial Resolution Remote Sensing Images, Geoscience and Remote Sensing, IEEE, 2012.

. Manish Mehta, Jorma Rissanen, Rakesh Agrawal: SLIQ: A Fast Scalable Classifier for Data Mining, IBM Almaden Reseach Center, 1996.

. Manish Mehta, Jorma Rissanen, Rakesh Agrawal: SPRINT: A Fast Scalable Classifier for Data Mining, IBM Almaden Reseach Center, 1996.

. Peer Fatima, Parveen, Dr. Mohamed Sathik: Fuzzy Decision Tree based Effective IMine Indexing, International Journal of Computer Technology and Electronics Engineering (IJCTEE),Volume 1, Issue 2, 2011.

. Quinlan J.R.: Simplifying decision trees, International Journal of Man-Machine Studies, no 27, pp. 221-234, 1987. http://www.mlrg.cecs.ucf.edu/MLRG_documents/c4.5.pdf

. Ricardo H.Tajiri, Eduardo Z. Marques, Bruno B. Z., Leonardo S. M.: A New Approach for Fuzzy Classification in Relational Databases, Database and Expert Systems Applications, Springer, pp. 511–518, 2011.

. Salvatore Ruggieri: Efficient C4.5, University Di Pisa, 2000.

. Wang T., Lee H.: Constructing a Fuzzy Decision Tree by Integrating Fuzzy Sets and Entropy, ACOS'06 Proceedings of the 5th WSEAS international conference on Applied computer science, World Scientific and Engineering Academy and Society, USA, 2006, pp. 306-311.

. Wei-Yuan Cheng, Chia-Feng Juang: A Fuzzy Model With Online Incremental SVM and Margin-Selective Gradient Descent Learning for Classification Problems, IEEE Transactions on Fuzzy systems, vol. 22, no. 2, pp 324-337, 2014.

. Zadeh L.A.: Fuzzy sets, Information and Control 8, pp.338-358, 1965.

. Zadeh L.A.: Fuzzy sets and fuzzy information granulation theory, Beijing Normal University Press, China, 2000.

. Zengchang Q., Jonathan Lawry: Linguistic Decision Tree Induction, Department of Engineering Mathematics, University of Bristol, United Kingdom, 2007.

. Zengchang Qin, Yongchuan Tang: Linguistic Decision Trees for Classification, Uncertainty Modeling for Data Mining, Springer, pp 77-119, 2014.

. Zhang, J. and Honavar: Learning Decision Tree Classifiers from Attribute-Value Taxonomies and Partially Specified Data, Proceedings of the International Conference on Machine Learning. Washington DC, 2003.


Full Text: Untitled PDF

Refbacks

  • There are currently no refbacks.


Journal of Computer Science and Cybernetics ISSN: 1813-9663

Published by Vietnam Academy of Science and Technology