Abstract
Together with increasing sizes of collected data, the problem of feature set reduction becomes more important. Machine learning methods, including classifiers, are sensitive to the training data. One of the known problems is called ’curse of dimensionality’. Some features (attributes) in the collection of data may not be informative so they obstruct the learning process. Removing them is very desirable from the classification quality point of view. In the paper we focus on wrapper approach to feature set reduction. We propose an evolutionary method to feature reduction by means of selection and construction. Genetic Algorithm is used as a tool for feature selection and Gene Expression Programming as a tool of dimensionality reduction by features construction. The paper presents the approach and the results of conducted experiments. Conclusions and future plans end the paper.
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References
Chao-Ton, S., Chien-Hsin, Y.: Feature selection for the SVM: An application to hypertension diagnosis. Expert Syst. Appl. 34(1), 754–763 (2008)
Dash, M., Huan Liu, H., Motoda, H.: Feature Selection Using Consistency Measure. In: Arikawa, S., Furukawa, K. (eds.) DS 1999. LNCS (LNAI), vol. 1721, p. 737. Springer, Heidelberg (1999)
Dash, M., Liu, H.: Feature Selection for Classification, Intelligent Data Analysis. Elsevier, Amsterdam (1997)
Ferreira, C.: Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems 13(2), 87–129 (2001), http://www.gene-expression-programming.com/webpapers/gepfirst.pdf
Ferreira, C.: Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. Springer, Heidelberg (2006)
Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.A.: Feature Extraction: Foundations and Applications. Springer, Heidelberg (2006)
Hall, M.A., Holmes, G.: Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Engineering 15(6), 1437–1447 (2003)
Jain Anil, K., Duin Robert, P.W., Jianchang, M.: Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 4–37 (2000)
Krawiec, K.: Genetic Programming-based Construction of Features for Machine Learning and Knowledge Discovery Tasks. In: Genetic Programming and Evolvable Machines, Kluwer Academic Publishers, Dordrecht (2002)
Liu, H.: Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17(4), 491–502 (2005)
Liu, H., Motoda, H.: Computational Methods of Feature Selection. Chapman & Hall/Crc Data Mining and Knowledge Discovery Series. Chapman and Hall/CRC, Boca Raton (2008)
Maimon, O., Rokach, L.: Data Mining and Knowledge Discovery Handbook. Springer Science & Business, Heidelberg (2005)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1999)
Mitchel, T.: Machine Learning. McGraw-Hill Science/Engineering/Math., New York (1997)
Mucciardi, A.N., Gose, E.E.: A Comparison of Seven Techniques for Choosing Subsets of Pattern Recognition Properties. IEEE Transactions on Computers 20(9), 1023–1031 (1971)
Muharram, M., Smith, G.D.: Evolutionary Constructive Induction. IEEE Transaction on Knowledge and Data Engineering 17(11), 1518–1528 (2005)
Otero, F.E.B., Silva, M.M.S., Freitas, A.A., Nievola, J.C.: EuroGP 2003. LNCS, vol. 2610, pp. 101–121. Springer, Heidelberg (2003)
Rayner, A.: DARA: Data Summarisation with Feature Construction, AICMS 2008. In: Second Asia International Conference on Modelling & Simulation, AMS, pp. 830–835 (2008)
Smith, M.G., Bull, L.: Genetic Programming with a Genetic Algorithm for Feature Construction and Selection. In: Genetic Programming and Evolvable Machines, Springer, Heidelberg (2005)
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Drozdz, K., Kwasnicka, H. (2010). Feature Set Reduction by Evolutionary Selection and Construction. In: Jędrzejowicz, P., Nguyen, N.T., Howlet, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2010. Lecture Notes in Computer Science(), vol 6071. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13541-5_15
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DOI: https://doi.org/10.1007/978-3-642-13541-5_15
Publisher Name: Springer, Berlin, Heidelberg
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