Combining Evolutionary and Sequential Search Strategies for Unsupervised Feature Selection

  • Artur Klepaczko
  • Andrzej Materka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6114)


The research presented in this paper aimed at development of a robust feature space exploration technique for unsupervised selection of its subspace for feature vectors classification. Experiments with synthetic and textured image data sets show that current sequential and evolutionary strategies are inefficient in the cases of large feature vector dimensions (reaching the order of 102) and multiple-class problems. Thus, the proposed approach utilizes the concept of hybrid genetic algorithm and adopts it for specific requirements of unsupervised learning.


Unsupervised feature selection hybrid genetic algorithm texture analysis 


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  1. 1.
    Kohavi, R., John, G.: Wrappers for feature subset selection. Artificial Intelligence 97(1-2), 273–324 (1997)zbMATHCrossRefGoogle Scholar
  2. 2.
    Davies, D., Bouldin, W.: A cluster separation measure. IEEE Trans. Pattern Analysis and Machine Intelligence 1(4), 224–227 (1979)CrossRefGoogle Scholar
  3. 3.
    Struyf, A., Hubert, M., Rousseeuw, P.: Integrating robust clustering techniques in s-plus. Computational Statistics & Data Analysis 26, 17–37 (1997)zbMATHCrossRefGoogle Scholar
  4. 4.
    Siedlecki, W., Sklansky, J.: On automatic feature selection. International Journal of Pattern Recognition and Artificial Intelligence 2(2), 197–220 (1988)CrossRefGoogle Scholar
  5. 5.
    Kim, Y., Street, W., Menczer, F.: Evolutionary model selection in unsupervised learning. Intelligent Data Analysis 6, 531–556 (2002)zbMATHGoogle Scholar
  6. 6.
    Goldberg, D.: Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  7. 7.
    Morita, M., Sabourin, R., Bortolozzi, F., Suen, C.: Unsupervised feature selection using multi-objective genetic algorithms for handwritten word recognition. In: Seventh IEEE Conf. Document Analysis and Recognition, pp. 666–670 (2003)Google Scholar
  8. 8.
    Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation 3(2), 221–248 (1994)CrossRefGoogle Scholar
  9. 9.
    Oh, I., Lee, J., Moon, B.: Hybrid genetic algorithms for feature selection. IEEE Trans. Pattern Analysis and Machine Intelligence 26(11), 1424–1437 (2004)CrossRefGoogle Scholar
  10. 10.
    Brodatz, P.: Textures. A Photographic Album for Artists and Designers. Dover, New York (1966)Google Scholar
  11. 11.
    Szczypinski, P., Strzelecki, M., Materka, A., Klepaczko, A.: Mazda - a software package for image texture analysis. Comput. Methods Prog. Biomed. 94, 66–76 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Artur Klepaczko
    • 1
  • Andrzej Materka
    • 1
  1. 1.Institute of ElectronicsTechnical University of LodzLodz

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