A HEDGE ALGEBRAS BASED REASONING METHOD FOR FUZZY RULE BASED CLASSIFIER

Phạm Đình Phong, Nguyễn Đức Dư, Hoàng Văn Thông

Abstract


The fuzzy rule based classifier (FRBC) design methods have intensively been being studied during last years. The ones designed by utilizing hedge algebras as a formalism to generate the optimal linguistic values along with their (triangular and trapezoidal) fuzzy sets based semantics for the FRBCs have been proposed. Those design methods generate the fuzzy sets based semantics because the classification reasoning method still bases on the fuzzy set theory.  One question which has been arisen is whether there is a pure hedge algebras classification reasoning method so that the fuzzy sets based semantic of the linguistic values in the fuzzy rule bases can be replaced with the hedge algebras based semantic. This paper answers that question by presenting a fuzzy rule based classifier design method based on hedge algebras with a pure hedge algebras classification reasoning method. The experimental results over 17 real world datasets are compared to the existing methods based on hedge algebras and fuzzy sets theory showing that the proposed method is effective and produces good results.

Keywords


fuzzy rule based classifier, hedge algebras, fuzziness measure, fuzziness intervals, semantically quantifying mapping value

Full Text:

PDF

References


Alcalá-Fdez J., Alcalá R., and Herrera F. - A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning, IEEE Transactions on Fuzzy System 19 (5) (2011) 857-872.

Alcalá R., Nojima Y., Herrera F., Ishibuchi H. - Multi-objective genetic fuzzy rule selection of single granularity-based fuzzy classification rules and its interaction with the lateral tuning of membership functions, Journal of Soft Computing 15 (12) (2011) 2303–2318.

Antonelli M., Ducange P., Marcelloni F. - A fast and efficient multi-objective evolutionary learning scheme for fuzzy rule-based classifiers, Information Sciences 283 (2014) 36–54.

Fazzolari M., Alcalá R., Herrera F. - A multi-objective evolutionary method for learning granularities based on fuzzy discretization to improve the accuracy-complexity trade-off of fuzzy rule-based classification systems: D-MOFARC algorithm, Applied Soft Computing 24 (2014) 470–481.

Ishibuchi H., Yamamoto T. - Fuzzy Rule Selection by Multi-Objective Genetic Local Search Algorithms and Rule Evaluation Measures in Data Mining, Fuzzy Sets and Systems 141 (1) (2004) 59-88.

Ishibuchi H., Yamamoto T. - Rule weight specification in fuzzy rule-based classification systems, IEEE Transactions on Fuzzy Systems 13 (4) (2005) 428–435.

Ishibuchi H., Nojima Y. - Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning, International Journal of Approximate Reasoning 44 (2007) 4–31.

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

Ho N. C., Wechler W. - Extended hedge algebras and their application to fuzzy logic, Fuzzy Sets and Systems 52 (1992) 259–281.

Ho N. C, Nam H. V., Khang D. T., Le H.C. - Hedge Algebras, Linguistic-valued logic and their application to fuzzy reasoning, Internat. J.Uncertain. Fuzziness Knowledge-Based Systems 7 (4) (1999) 347–361.

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

Ho N. C. - A topological completion of refined hedge algebras and a model of fuzziness of linguistic terms and hedges, Fuzzy Sets and Systems 158 (2007) 436–451.

Ho N. C., Pedrycz W., Long D. T., Son T. T. - A genetic design of linguistic terms for fuzzy rule based classifiers, International Journal of Approximate Reasoning 54 (1) (2013) 1-21.

Ho N. C., Son T. T., Phong P. D. - Modeling of a semantics core of linguistic terms based on an extension of hedge algebra semantics and its application, Knowledge-Based Systems 67 (2014) 244–262.

Ho N. C., Thong H. V., Long N. V. - A discussion on interpretability of linguistic rule based systems and its application to solve regression problems, Knowledge-Based Systems 88 (2015) 107–133.

Le B. H., Ho N. C., Lan V. N., Hung N. C. - General design method of hedge-algebras-based fuzzy controllers and an application for structural active control, Applied Intelligence 43 (2) (2015) 251–275.

Lan V. N., Ha T. T., Lai L. K., Duy N. T. - The application of the hedge algebras in forecast control based on the models, In Proceedings of The 11st National Conference on Fundamental and Applied IT Research, Hanoi, Vietnam (2018) 521-528.

Phong P. D., Ho N. C., Thuy N. T. - Multi-objective Particle Swarm Optimization Algorithm and its Application to the Fuzzy Rule Based Classifier Design Problem with the Order Based Semantics of Linguistic Terms, In Proceedings of The 10th IEEE RIVF International Conference on Computing and Communication Technologies (RIVF-2013), Hanoi, Vietnam (2013) 12 – 17.

Maximino S. L. - Multi-Objective Optimization using Sharing in Swarm Optimization Algorithms, Doctor thesis, School of Computer Science, The University of Birmingham (2006).

Demˇsar J. - Statistical Comparisons of Classifiers over Multiple Data Sets, Journal of Machine Learning Research 7 (2006) 1–30.




DOI: https://doi.org/10.15625/2525-2518/57/5/13811 Display counter: Abstract : 46 views. PDF : 20 views.

Refbacks

  • There are currently no refbacks.


Index: Google Scholar; Crossref; VCGate; Asean Citation Index

Published by Vietnam Academy of Science and Technology