A Novel Modified Adaptive Fuzzy Inference Engine and Its Application to Pattern Classification
The Neuro-Fuzzy hybridization scheme has become
of research interest in pattern classification over the past decade. The
present paper proposes a novel Modified Adaptive Fuzzy Inference
Engine (MAFIE) for pattern classification. A modified Apriori
algorithm technique is utilized to reduce a minimal set of decision
rules based on input output data sets. A TSK type fuzzy inference
system is constructed by the automatic generation of membership
functions and rules by the fuzzy c-means clustering and Apriori
algorithm technique, respectively. The generated adaptive fuzzy
inference engine is adjusted by the least-squares fit and a conjugate
gradient descent algorithm towards better performance with a
minimal set of rules. The proposed MAFIE is able to reduce the
number of rules which increases exponentially when more input
variables are involved. The performance of the proposed MAFIE is
compared with other existing applications of pattern classification
schemes using Fisher-s Iris and Wisconsin breast cancer data sets and
shown to be very competitive.
[1] P.K. Simpson, "Fuzzy Min-Max Neural Networks-Part
1:Classification," IEEE Transaction on Neural Networks, vol. 3, no.5,
pp.776-786, Sept. 1992.
[2] S. Abe and M.S. Lan, "Fuzzy Rules Extraction Directly from Numerical
Data for Function Approximation," IEEE Transaction on System, Man,
and Cybernetics, vol. 25, no.1, pp.119-129, Jan. 1995.
[3] G.O.A. Zapata, R.K.H. Galvao, and T. Yoneyama, "Extracting Fuzzy
Control Rules from Experimental Human Operator Data," IEEE
Transaction on System, Man and Cybernetics - Part B: Cybernetics, vo.
29, no. 3, pp 25-40, Feb. 1999.
[4] Han, J. and Kamber, M., Data Mining: Concepts and Techniques,
Second Edition, Morgan Kaufmann publishers, San Francisco, 2006
[5] R. Agrawal, R. Srikant, "Fast Algorithms for Mining Association
Rules", Proceedings of the 20th VLDB Conference, Santiago, Chile,
1994.
[6] Bilal I. S., Keshav P. D., Alamgir M. H., Mohammad S A., "
Diversification of Fuzzy Association Rules to Improve Prediction
Accuracy", Fuzzy Systems (Fuzz) in IEEE Explorer, 2010.
[7] T. Takagi and M. Sugeno, "Fuzzy identification of systems and its
applications to modeling and control," IEEE Transaction on Systems,
Man, and Cybernetics, vol. SMC-15, pp. 116-132, Jan.-Feb. 1985
[8] X. Zeng and M. G. Singh,, "Approximation Theory of Fuzzy Systems-
MIMO Case IEEE Transactions on Fuzzy Systems, vol. 3, no. 2, May
1995.
[9] James C. Bezdek, Pattern Recognition with Fuzzy Objective Function
Algorithms, Plenum Press, pp.65-86, 1981.
[10] Mohanad A., Mohammad M., Abdullah R., " Optimizing of Fuzzy CMeans
Clustering Algorithm Using GA", World Academy of Science,
Engineering and Technology, Vol. 39, 2008.
[11] C.L. Blake and C.J. Merz, "UCI Repository of Machine Learning
Databases," University of California, Irvine, Department of Information
and Computer Science, http://www.ics.uci.edu/~mlearn
/MLRepository.html, 1998
[12] S. Fahlman and C. Lebiere, "The Cascade-Correlation Learning
Architecture," Carnegie Melloin Univ., School of Computer Science,
Technical Report CMU-CS- 90-100, Feb. 1990.
[13] T-P. Hong and S.-S. Tseng, "A Generalised Version Space Learning
Algorithm for Noisy and Uncertain Data," IEEE Transaction on
Knowledge and Data Eng., vol. 9, no. 2, pp. 336-340, Mar.-Apr. 1997.
[14] S.C. Newton, S. Pemmaraju, and S. Mitra, "Adaptive Fuzzy Leader
Clustering of Complex Data Sets in Pattern Recognition," IEEE
Transaction on Neural Networks, vol. 3, no.5, pp.794-800, Sept. 1992.
[15] T.P. Wu and S.M. Chen, "A New Method for Constructing Membership
Functions and Fuzzy Rules from Training Examples," IEEE Transaction
on System, Man, and Cybernetics - Part B: Cybernetics, vol. 29, no.1,
pp.25-40, Feb. 1999.
[16] R. Setiono, "Extracting M-of-N Rules from Trained Neural Networks,"
IEEE Transaction On Neural Networks, vol. 11, no. 2, pp.512- 519, Mar.
2000.
[17] B.C. Lovel and A.P. Bradley, "The Multiscale Classifier," IEEE
Transaction On Pattern Analysis and Machine Intelligence, vol. 18, no.
2, pp. 124-137, Feb. 1996.
[18] H.-M. Lee, C.-M. Chen, J.-M. Chen, and Y.-L. Jou, "An Efficient Fuzzy
Classifier with Feature Selection Based on Fuzzy Entropy," IEEE
Transaction on Systems, Man, and Cybernetics - Part B: Cybernetics,
vol. 31, no.3, pp.426-432, June 2001.
[19] A. Chatterjee and A. Rakshit, "Influential Rule Search Scheme (IRSS) -
A New Fuzzy Pattern Classifier," IEEE Transaction on Knowledge and
Data Engineering, vol. 16, no. 8, pp. 881-893Aug. 2004.
[20] ChangSu L., Anthony. Z., Tomas B., "An Adaptive T-Stype Rough-
Fuzzy Inference System (ARFIS) for Pattern Classification", Fuzzy
Information Society, IEEE Explorer, pp. 117-122, 2007.
[21] Sandeep C., and Rene V. M., "RANFIS: Rough Adaptive Neuro-Fuzzy
Inference System", International Journal of Computational Intelligence,
vol. 3, No. 4, 2006.
[1] P.K. Simpson, "Fuzzy Min-Max Neural Networks-Part
1:Classification," IEEE Transaction on Neural Networks, vol. 3, no.5,
pp.776-786, Sept. 1992.
[2] S. Abe and M.S. Lan, "Fuzzy Rules Extraction Directly from Numerical
Data for Function Approximation," IEEE Transaction on System, Man,
and Cybernetics, vol. 25, no.1, pp.119-129, Jan. 1995.
[3] G.O.A. Zapata, R.K.H. Galvao, and T. Yoneyama, "Extracting Fuzzy
Control Rules from Experimental Human Operator Data," IEEE
Transaction on System, Man and Cybernetics - Part B: Cybernetics, vo.
29, no. 3, pp 25-40, Feb. 1999.
[4] Han, J. and Kamber, M., Data Mining: Concepts and Techniques,
Second Edition, Morgan Kaufmann publishers, San Francisco, 2006
[5] R. Agrawal, R. Srikant, "Fast Algorithms for Mining Association
Rules", Proceedings of the 20th VLDB Conference, Santiago, Chile,
1994.
[6] Bilal I. S., Keshav P. D., Alamgir M. H., Mohammad S A., "
Diversification of Fuzzy Association Rules to Improve Prediction
Accuracy", Fuzzy Systems (Fuzz) in IEEE Explorer, 2010.
[7] T. Takagi and M. Sugeno, "Fuzzy identification of systems and its
applications to modeling and control," IEEE Transaction on Systems,
Man, and Cybernetics, vol. SMC-15, pp. 116-132, Jan.-Feb. 1985
[8] X. Zeng and M. G. Singh,, "Approximation Theory of Fuzzy Systems-
MIMO Case IEEE Transactions on Fuzzy Systems, vol. 3, no. 2, May
1995.
[9] James C. Bezdek, Pattern Recognition with Fuzzy Objective Function
Algorithms, Plenum Press, pp.65-86, 1981.
[10] Mohanad A., Mohammad M., Abdullah R., " Optimizing of Fuzzy CMeans
Clustering Algorithm Using GA", World Academy of Science,
Engineering and Technology, Vol. 39, 2008.
[11] C.L. Blake and C.J. Merz, "UCI Repository of Machine Learning
Databases," University of California, Irvine, Department of Information
and Computer Science, http://www.ics.uci.edu/~mlearn
/MLRepository.html, 1998
[12] S. Fahlman and C. Lebiere, "The Cascade-Correlation Learning
Architecture," Carnegie Melloin Univ., School of Computer Science,
Technical Report CMU-CS- 90-100, Feb. 1990.
[13] T-P. Hong and S.-S. Tseng, "A Generalised Version Space Learning
Algorithm for Noisy and Uncertain Data," IEEE Transaction on
Knowledge and Data Eng., vol. 9, no. 2, pp. 336-340, Mar.-Apr. 1997.
[14] S.C. Newton, S. Pemmaraju, and S. Mitra, "Adaptive Fuzzy Leader
Clustering of Complex Data Sets in Pattern Recognition," IEEE
Transaction on Neural Networks, vol. 3, no.5, pp.794-800, Sept. 1992.
[15] T.P. Wu and S.M. Chen, "A New Method for Constructing Membership
Functions and Fuzzy Rules from Training Examples," IEEE Transaction
on System, Man, and Cybernetics - Part B: Cybernetics, vol. 29, no.1,
pp.25-40, Feb. 1999.
[16] R. Setiono, "Extracting M-of-N Rules from Trained Neural Networks,"
IEEE Transaction On Neural Networks, vol. 11, no. 2, pp.512- 519, Mar.
2000.
[17] B.C. Lovel and A.P. Bradley, "The Multiscale Classifier," IEEE
Transaction On Pattern Analysis and Machine Intelligence, vol. 18, no.
2, pp. 124-137, Feb. 1996.
[18] H.-M. Lee, C.-M. Chen, J.-M. Chen, and Y.-L. Jou, "An Efficient Fuzzy
Classifier with Feature Selection Based on Fuzzy Entropy," IEEE
Transaction on Systems, Man, and Cybernetics - Part B: Cybernetics,
vol. 31, no.3, pp.426-432, June 2001.
[19] A. Chatterjee and A. Rakshit, "Influential Rule Search Scheme (IRSS) -
A New Fuzzy Pattern Classifier," IEEE Transaction on Knowledge and
Data Engineering, vol. 16, no. 8, pp. 881-893Aug. 2004.
[20] ChangSu L., Anthony. Z., Tomas B., "An Adaptive T-Stype Rough-
Fuzzy Inference System (ARFIS) for Pattern Classification", Fuzzy
Information Society, IEEE Explorer, pp. 117-122, 2007.
[21] Sandeep C., and Rene V. M., "RANFIS: Rough Adaptive Neuro-Fuzzy
Inference System", International Journal of Computational Intelligence,
vol. 3, No. 4, 2006.
@article{"International Journal of Information, Control and Computer Sciences:61151", author = "J. Hossen and A. Rahman and K. Samsudin and F. Rokhani and S. Sayeed and R. Hasan", title = "A Novel Modified Adaptive Fuzzy Inference Engine and Its Application to Pattern Classification", abstract = "The Neuro-Fuzzy hybridization scheme has become
of research interest in pattern classification over the past decade. The
present paper proposes a novel Modified Adaptive Fuzzy Inference
Engine (MAFIE) for pattern classification. A modified Apriori
algorithm technique is utilized to reduce a minimal set of decision
rules based on input output data sets. A TSK type fuzzy inference
system is constructed by the automatic generation of membership
functions and rules by the fuzzy c-means clustering and Apriori
algorithm technique, respectively. The generated adaptive fuzzy
inference engine is adjusted by the least-squares fit and a conjugate
gradient descent algorithm towards better performance with a
minimal set of rules. The proposed MAFIE is able to reduce the
number of rules which increases exponentially when more input
variables are involved. The performance of the proposed MAFIE is
compared with other existing applications of pattern classification
schemes using Fisher-s Iris and Wisconsin breast cancer data sets and
shown to be very competitive.", keywords = "Apriori algorithm, Fuzzy C-means, MAFIE, TSK", volume = "5", number = "8", pages = "937-6", }
