Abstract
There are various techniques for data mining and data analysis. Among them, hybrid approaches combining two or more algorithms gain importance as the complexity and dimension of real world data sets grows. In this paper, we present an application of evolutionary-fuzzy classification technique for data mining. Genetic programming is deployed to evolve a fuzzy classifier and an example of real world application is presented.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Affenzeller, M., Winkler, S., Wagner, S., Beham, A.: Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications. Chapman & Hall/CRC (2009)
Bezdek, J.C., Keller, J., Krisnapuram, R., Pal, N.R.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing (The Handbooks of Fuzzy Sets). Springer-Verlag New York, Inc., Secaucus (2005)
Bodenhofer, U.: Genetic Algorithms: Theory and Applications. Lecture notes, Fuzzy Logic Laboratorium Linz-Hagenberg (Winter 2003/2004)
Cordón, O., de Moya, F., Zarco, C.: Fuzzy logic and multiobjective evolutionary algorithms as soft computing tools for persistent query learning in text retrieval environments. In: IEEE Int. Conference on Fuzzy Systems 2004, Budapest, Hungary, pp. 571–576 (2004)
Crestani, F., Pasi, G.: Soft information retrieval: Applications of fuzzy set theory and neural networks. In: Kasabov, N., Kozma, R. (eds.) Neuro-Fuzzy Techniques for Intelligent Information Systems, pp. 287–315. Springer, Heidelberg (1999)
Húsek, D., Owais, S.S.J., Snášel, V., Krömer, P.: Boolean queries optimization by genetic programming. Neural Network World, 359–409 (2005)
Húsek, D., Snášel, V., Neruda, R., Owais, S.S.J., Krömer, P.: Boolean queries optimization by genetic programming. WSEAS Trans. on Inf. Sci. and Applications 3(1), 15–20 (2006)
Jantzen, J.: Tutorial On Fuzzy Logic. Technical Report 98-E-868 (logic), Technical University of Denmark, Dept. of Automation (1998)
Johnson, C.G.: Artificial Immune System Programming for Symbolic Regression. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 345–353. Springer, Heidelberg (2003)
Jones, G.: Genetic and evolutionary algorithms. In: von Rague, P. (ed.) Encyclopedia of Computational Chemistry. John Wiley and Sons (1998)
Koza, J.: Genetic programming: A paradigm for genetically breeding populations of computer programs to solve problems. Technical Report STAN-CS-90-1314, Dept. of Computer Science, Stanford University (1990)
Koza, J.R., Andre, D., Bennett, F.H., Keane, M.A.: Genetic Programming III: Darwinian Invention & Problem Solving, 1st edn. Morgan Kaufmann Publishers Inc., San Francisco (1999)
Koza, J.R., Keane, M.A., Streeter, M.J.: Evolving inventions. Scientific American (2003)
Kraft, D.H., Petry, F.E., Buckles, B.P., Sadasivan, T.: Genetic Algorithms for Query Optimization in Information Retrieval: Relevance Feedback. In: Sanchez, E., Shibata, T., Zadeh, L. (eds.) Genetic Algorithms and Fuzzy Logic Systems, World Scientific, Singapore (1997)
O’Sullivan, J., Ryan, C.: An Investigation into the Use of Different Search Strategies with Grammatical Evolution. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A.G.B. (eds.) EuroGP 2002. LNCS, vol. 2278, pp. 268–277. Springer, Heidelberg (2002)
Ryan, C., Collins, J.J., O’Neill, M.: Grammatical Evolution: Evolving Programs for an Arbitrary Language. In: Banzhaf, W., Poli, R., Schoenauer, M., Fogarty, T.C. (eds.) EuroGP 1998. LNCS, vol. 1391, pp. 83–96. Springer, Heidelberg (1998)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing and Management 24(5), 513–523 (1988)
Snasel, V., Abraham, A., Owais, S., Platos, J., Kromer, P.: User Profiles Modeling in Information Retrieval Systems. In: Emergent Web Intelligence: Advanced Information Retrieval. Advanced Inf. and Knowledge Proc., pp. 169–198. Springer, London (2010)
Snášel, V., Krömer, P., Platoš, J., Abraham, A.: The Evolution of Fuzzy Classifier for Data Mining with Applications. In: Deb, K., Bhattacharya, A., Chakraborti, N., Chakroborty, P., Das, S., Dutta, J., Gupta, S.K., Jain, A., Aggarwal, V., Branke, J., Louis, S.J., Tan, K.C. (eds.) SEAL 2010. LNCS, vol. 6457, pp. 349–358. Springer, Heidelberg (2010)
Verikas, A., Guzaitis, J., Gelzinis, A., Bacauskiene, M.: A general framework for designing a fuzzy rule-based classifier. Knowledge and Information Systems, 1–19
Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)
Zadeh, L.A.: Test-score semantics dor natural languages and meaning representation via Pruf. In: Empirical Semantics, Quantitative Semantics, vol. 12(1), pp. 281–349. Studienverlag Brockmeyer, Bochum (1981)
Zelinka, I., Davendra, D., Senkerik, R., Jasek, R., Oplatkova, Z.: Analytical programming - a novel approach for evolutionary synthesis of symbolic structures, In: Kita, E. (ed.) Evolutionary Algorithms, InTech
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Owais, S., Krömer, P., Platoš, J., Snášel, V., Zelinka, I. (2013). Data Mining by Symbolic Fuzzy Classifiers and Genetic Programming. In: Zelinka, I., Rössler, O., Snášel, V., Abraham, A., Corchado, E. (eds) Nostradamus: Modern Methods of Prediction, Modeling and Analysis of Nonlinear Systems. Advances in Intelligent Systems and Computing, vol 192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33227-2_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-33227-2_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33226-5
Online ISBN: 978-3-642-33227-2
eBook Packages: EngineeringEngineering (R0)