Automatic Synthesis of Fuzzy Inference Systems for Classification

This work introduces AutoFIS-Class, a methodology for automatic synthesis of Fuzzy Inference Systems for classification problems. It is a data-driven approach, which can be described in five steps: (i) mapping of each pattern to a membership degree to fuzzy sets; (ii) generation of a set of fuzzy rule premises, inspired on a search tree, and application of quality criteria to reduce the exponential growth; (iii) association of a given premise to a suitable consequent term; (iv) aggregation of fuzzy rules to a same class and (v) decision on which consequent class is most compatible with a given pattern. The performance of AutoFIS-Class has been compared to those of other four rule-based systems for 21 datasets. Results show that AutoFIS-Class is competitive with respect to those systems, most of them evolutionary ones.

[1]  Francisco Herrera,et al.  A Review of the Application of Multiobjective Evolutionary Fuzzy Systems: Current Status and Further Directions , 2013, IEEE Transactions on Fuzzy Systems.

[2]  Edwin Lughofer,et al.  Evolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications , 2011, Studies in Fuzziness and Soft Computing.

[3]  Ricardo Tanscheit,et al.  GPFIS-CLASS: A Genetic Fuzzy System based on Genetic Programming for classification problems , 2015, Appl. Soft Comput..

[4]  Xiao-Jun Zeng,et al.  An improved approach of self-organising fuzzy neural network based on similarity measures , 2012, Evol. Syst..

[5]  Oscar Cordón,et al.  International Journal of Approximate Reasoning a Historical Review of Evolutionary Learning Methods for Mamdani-type Fuzzy Rule-based Systems: Designing Interpretable Genetic Fuzzy Systems , 2022 .

[6]  Jesús Alcalá-Fdez,et al.  A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning , 2011, IEEE Transactions on Fuzzy Systems.

[7]  Jesús Alcalá-Fdez,et al.  KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework , 2011, J. Multiple Valued Log. Soft Comput..

[8]  Francisco Herrera,et al.  Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures , 2011, Inf. Sci..

[9]  Plamen Angelov,et al.  Evolving Takagi‐Sugeno Fuzzy Systems from Streaming Data (eTS+) , 2010 .

[10]  Plamen P. Angelov,et al.  Evolving Fuzzy-Rule-Based Classifiers From Data Streams , 2008, IEEE Transactions on Fuzzy Systems.

[11]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[12]  Hisao Ishibuchi,et al.  Rule weight update in parallel distributed fuzzy genetics-based machine learning with data rotation , 2013, 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[13]  Francisco Herrera,et al.  Solving multi-class problems with linguistic fuzzy rule based classification systems based on pairwise learning and preference relations , 2010, Fuzzy Sets Syst..

[14]  Daniel F. Leite,et al.  Evolving fuzzy granular modeling from nonstationary fuzzy data streams , 2012, Evol. Syst..

[15]  Antonio J. Rivera,et al.  GP-COACH: Genetic Programming-based learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems , 2010, Inf. Sci..

[16]  E. Lughofer,et al.  Evolving fuzzy classifiers using different model architectures , 2008, Fuzzy Sets Syst..

[17]  Massimiliano Daniele Rosini,et al.  Macroscopic Models for Vehicular Flows and Crowd Dynamics: Theory and Applications: Classical and Non–Classical Advanced Mathematics for Real Life Applications , 2013 .

[18]  José M. Alonso,et al.  Looking for a good fuzzy system interpretability index: An experimental approach , 2009, Int. J. Approx. Reason..

[19]  W. J. Conover,et al.  Practical Nonparametric Statistics , 1972 .

[20]  Mahardhika Pratama,et al.  Generalized smart evolving fuzzy systems , 2015, Evol. Syst..

[21]  Hisao Ishibuchi,et al.  Rule weight specification in fuzzy rule-based classification systems , 2005, IEEE Transactions on Fuzzy Systems.

[22]  Francisco Herrera,et al.  Genetic fuzzy systems: taxonomy, current research trends and prospects , 2008, Evol. Intell..

[23]  Edwin Lughofer,et al.  On-line assurance of interpretability criteria in evolving fuzzy systems - Achievements, new concepts and open issues , 2013, Inf. Sci..

[24]  Thair Nu Phyu.  Survey of Classification Techniques in Data Mining , 2009 .

[25]  Francesc Esteva,et al.  Review of Triangular norms by E. P. Klement, R. Mesiar and E. Pap. Kluwer Academic Publishers , 2003 .

[26]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[27]  R. Mesiar,et al.  Aggregation operators: properties, classes and construction methods , 2002 .