Genetic type-2 fuzzy classifier functions

A new type-2 fuzzy classifier function system is proposed for uncertainty modeling using genetic algorithms - GT2FCF. Proposed method implements a three-phase learning strategy to capture the uncertainties in fuzzy classifier function systems induced by learning parameters, as well as fuzzy classifier functions. Hidden structures are captured with the implementation of improved fuzzy clustering. The optimum uncertainty interval of the type-2 fuzzy membership values are captured with a genetic learning algorithm. The results of the experiments show that the GT2FCF is comparable - if not superior- to well-known benchmark methods in terms of area under the receiver operating curve (AUC) performance measure.

[1]  Oscar Montiel,et al.  Evolutionary optimization of interval type-2 membership functions using the Human Evolutionary Model , 2007, 2007 IEEE International Fuzzy Systems Conference.

[2]  I. Burhan Türksen,et al.  Increasing accuracy of two-class pattern recognition with enhanced fuzzy functions , 2009, Expert Syst. Appl..

[3]  Jerry M. Mendel,et al.  Interval type-2 fuzzy logic systems , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[4]  I. Burhan Türksen,et al.  Uncertainty Modeling of Improved Fuzzy Functions With Evolutionary Systems , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Chi-Hsu Wang,et al.  Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN) , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[6]  Gerardo M. Mendez,et al.  Hybrid Learning Algorithm for Interval Type-2 Fuzzy Logic Systems , 2006, Control. Intell. Syst..

[7]  I. Burhan Türksen,et al.  Validation criteria for enhanced fuzzy clustering , 2008, Pattern Recognit. Lett..

[8]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[9]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[10]  Oscar Castillo,et al.  Interval Type-2 TSK Fuzzy Logic Systems Using Hybrid Learning Algorithm , 2005, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05..

[11]  I. Turksen.  Type 2 representation and reasoning for CWW , 2002 .

[12]  Hani Hagras.  Comments on "Dynamical Optimal Training for Interval Type-2 Fuzzy Neural Network (T2FNN) , 2006, IEEE Trans. Syst. Man Cybern. Part B.

[13]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[14]  I. Burhan Türksen,et al.  Enhanced Fuzzy System Models With Improved Fuzzy Clustering Algorithm , 2008, IEEE Transactions on Fuzzy Systems.

[15]  I. Burhan Türksen,et al.  Discrete Interval Type 2 Fuzzy System Models Using Uncertainty in Learning Parameters , 2007, IEEE Transactions on Fuzzy Systems.

[16]  W. TanW.,et al.  Uncertain Rule-Based Fuzzy Logic Systems , 2007 .

[17]  Frank Chung-Hoon Rhee,et al.  Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to $C$-Means , 2007, IEEE Transactions on Fuzzy Systems.

[18]  Jerry M. Mendel,et al.  Type-2 Fuzzy Logic Systems : Type- , 1998 .

[19]  Asli Celikyilmaz,et al.  Enhanced Type 2 Fuzzy System Models with Improved Fuzzy Functions , 2007, NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society.

[20]  Jujang Lee,et al.  Adaptive network-based fuzzy inference system with pruning , 2003, SICE 2003 Annual Conference (IEEE Cat. No.03TH8734).

[21]  Hani Hagras,et al.  A Genetic Algorithm Based Architecture for Evolving Type-2 Fuzzy Logic Controllers for Real World Autonomous Mobile Robots , 2007, 2007 IEEE International Fuzzy Systems Conference.

[22]  I. Burhan Türksen,et al.  Fuzzy functions with LSE , 2008, Appl. Soft Comput..

[23]  James C. Bezdek,et al.  Fuzzy mathematics in pattern classification , 1973 .