Acta Univ. Agric. Silvic. Mendelianae Brun. 2013, 61(7), 2445-2449 | DOI: 10.11118/actaun201361072445

Classification of economic data into multiple classes by means of evolutionary methods

Jiří Lýsek, Jiří ©»astný
Department of Informatics, Faculty of Business and Economics, Mendel University in Brno, 613 00 Brno, Czech Republic

In this contribution we deal with an automatic classification of economic data into multiple classes. A classifier created by grammatical evolution is used to determine the data sample membership into one of the defined classes. The grammar rules used for classifier structure creation are presented. The performance of our classifier is compared with multilayer perceptron neural network classifier and Kohonen neural network classifier. We used a survey data of consumer behaviour in food market in Czech Republic.

Keywords: genetic algorithms, classification, grammatical evolution, learning

Received: April 11, 2013; Published: December 24, 2013  Show citation

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Lýsek, J., & ©»astný, J. (2013). Classification of economic data into multiple classes by means of evolutionary methods. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 61(7), 2445-2449. doi: 10.11118/actaun201361072445
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    References

    1. CIESIELSKI, V. B., ANDREAE, P., ZHANG M., 2003: A Domain-Independent Window Approach to Multiclass Object Detection Using Genetic Programming. EURASIP Journal on Applied Signal Processing, 2003, 8: 841-859. ISSN 1687-6180. Go to original source...
    2. HOPCROFT, J. E., ULLMAN, J. D., 1979: Introduction to Automata Theory, Languages and Computation. Boston: Addison-Wesley, 521 p. ISBN 978-0201441246.
    3. KOZA, R. J., 1992: Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge: The MIT Press, 819 p. ISBN 0-262-11170-5.
    4. LÝSEK, J., ©«ASTNÝ, J., MOTYČKA, A., 2012: Object Recognition by means of Evolved Detector and Classifier Program. In: 18th International Conference of Soft Computing MENDEL 2012, Brno: Brno University of Technology, pp. 82-87. ISSN 1803-3814.
    5. O©MERA, P., POPELKA, O., PIVOŇKA, P., 2006: Parallel Grammatical evolution with backward processing. In: CHEAH, C. C., 9th International Conference on Control, Automation, Robotics and Vision. Singapore, New York: IEEE, pp. 949-954, ISBN 978-1-4244-0341-7. Go to original source...
    6. POPELKA, O., ©«ASTNÝ, J., 2009: Uplatnění metod umělé inteligence v zemědělsko-ekonomických predikčních úlohách. Brno: Mendelova univerzita v Brně, 48 p. ISBN 978-80-7375-340-5.
    7. RYAN, C., O'NEILL, M., 2003: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Boston: Kluwer, 144 p. ISBN 978-1402074448.
    8. SILVA, S., 2011: Reassembling Operator Equalisation - A Secret Revealed. SIGEVOlution, 5, 3: 10-22, ISSN 1931-8499. DOI: 10.1145/2043118.2043120 Go to original source...
    9. ©«ASTNÝ, J., TURČÍNEK, P., MOTYČKA, A., 2011: Using Neural Networks for Marketing Research Data Classification. Mathematical Methods and Techniques in Engeneering & Environmental Science. Catania, Sicily, Italy: WSEAS Press, pp. 252-256. ISBN 978-1-61804-046-6.
    10. ©KORPIL, V., LÝSEK, J., MOTYČKA, A., CEPL, M., ENDRLE, P., 2012: Grammatical Evolution as a Learning Process for Multiclass Object Detection. In: Recent Researches in Communications and Computers, Kos, Greece: WSEAS Press, pp. 101-105. ISBN 978-1-61804-108-1.

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