Pattern Synthesis Using Fuzzy Partitions of the Feature Set for Nearest Neighbor Classifier Design

  • Pulabaigari Viswanath
  • S. Chennakesalu
  • R. Rajkumar
  • M. Raja Sekhar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7080)


Nearest neighbor classifiers require a larger training set in order to achieve a better classification accuracy. For a higher dimensional data, if the training set size is small, it suffers from the curse of dimensionality effect and performance gets degraded. Partition based pattern synthesis is an existing technique of generating a larger set of artificial training patterns based on a chosen partition of the feature set. If the blocks of the partition are statistically independent then the quality of synthetic patterns generated is high. But, such a partition, often does not exist for real world problems. So, approximate ways of generating a partition based on correlation coefficient values between pairs of features were used earlier in some studies. That is, an approximate hard partition, where each feature belongs to exactly one cluster (block) of the partition was used for doing the synthesis. The current paper proposes an improvement over this. Instead of having a hard approximate partition, a soft approximate partition based on fuzzy set theory could be beneficial. The present paper proposes such a fuzzy partitioning method of the feature set called fuzzy partition around medoids (fuzzy-PAM). Experimentally, using some standard data-sets, it is demonstrated that the fuzzy partition based synthetic patters are better as for as the classification accuracy is concerned.


Pattern synthesis fuzzy partition nearest neighbor classifier partition around medoids 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ananthanarayana, V., Murty, M., Subramanian, D.: An incremental data mining algorithm for compact realization of prototypes. Pattern Recognition 34, 2249–2251 (2001)CrossRefzbMATHGoogle Scholar
  2. 2.
    Babu, T.R., Murty, M.N.: Comparison of genetic algorithms based prototype selection schemes. Pattern Recognition 34, 523–525 (2001)CrossRefGoogle Scholar
  3. 3.
    Bellman, R.: Adaptive Control Processes: A Guided Tour. Princeton University Press, New Jersey (1961)CrossRefzbMATHGoogle Scholar
  4. 4.
    Bishop, C.: Neural Networks for Pattern Recognition. Clarendon Press, Oxford (1995)zbMATHGoogle Scholar
  5. 5.
    Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)CrossRefzbMATHGoogle Scholar
  6. 6.
    Dasarathy, B.V.: Nearest neighbor (NN) norms: NN pattern classification techniques. IEEE Computer Society Press, Los Alamitos (1991)Google Scholar
  7. 7.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. A Wiley-interscience Publication, John Wiley & Sons (2000)Google Scholar
  8. 8.
    Hamamoto, Y., Uchimura, S., Tomita, S.: A bootstrap technique for nearest neighbor classifier design. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(1), 73–79 (1997)CrossRefGoogle Scholar
  9. 9.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Academic Press (2001)Google Scholar
  10. 10.
    Murphy, P.M.: UCI Repository of Machine Learning Databases. Department of Information and Computer Science, University of California, Irvine, CA (2000),
  11. 11.
    Viswanath, P., Murty, M., Bhatnagar, S.: Fusion of multiple approximate nearest neighbor classifiers for fast and efficient classification. Information Fusion 5(4), 239–250 (2004)CrossRefGoogle Scholar
  12. 12.
    Viswanath, P., Murty, M., Bhatnagar, S.: A pattern synthesis technique with an efficient nearest neighbor classifier for binary pattern recognition. In: Proceedings of the 17 th International Conference on Pattern Recognition (ICPR 2004), Cambridge, UK, vol. 4, pp. 416–419 (2004)Google Scholar
  13. 13.
    Viswanath, P., Murty, M., Bhatnagar, S.: Overlap pattern synthesis with an efficient nearest neighbor classifier. Pattern Recognition 38, 1187–1195 (2005)CrossRefzbMATHGoogle Scholar
  14. 14.
    Viswanath, P., Murty, M., Bhatnagar, S.: Partition based pattern synthesis technique with efficient algorithms for nearest neighbor classification. Pattern Recognition Letters 27, 1714–1724 (2006)CrossRefGoogle Scholar
  15. 15.
    Viswanath, P., Murty, M., Kambala, S.: An Efficient Parzen-Window Based Network Intrusion Detector Using a Pattern Synthesis Technique. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PREMI 2005. LNCS, vol. 3776, pp. 799–804. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Pulabaigari Viswanath
    • 1
  • S. Chennakesalu
    • 1
  • R. Rajkumar
    • 1
  • M. Raja Sekhar
    • 1
  1. 1.Department of Computer Science and EngineeringRajeev Gandhi Memorial College of Engineering & TechnologyNandyalIndia

Personalised recommendations