A Novel Strategy for Designing Efficient Multiple Classifier

  • Rohit Singh
  • Sandeep Samal
  • Tapobrata Lahiri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)


In this paper we have shown that systematic incorporation of decision from various classifiers following a simple decision decomposition rule, gives better decision in comparison to the existing multiple classifier systems. In our method each classifier were graded according to their effectiveness of providing more accurate results. This approach first utilizes the best classifier. If this classifier classifies the test sample into more than one class or fails to classify the test data then the feature next to the best is summoned to finish up the remaining part of the classification. The continuation of this process, along with the judicious selection of classifiers, yields better efficiency in identifying a single class for the test data. The results obtained after the experiments on a set of fingerprint images shows the effectiveness of our proposed classifier.


Wavelet Coefficient Input Pattern Multiple Classifier Class Boundary Class Center 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Pudil, P., Novovicova, J., Blaha, S., Kittler, J.: Multistage Pattern Recognition with Reject Option. In: Proc. 11th IAPR Int’l Conf. Pattern Recognition, Conf. B: Pattern Recognition Methodology and Systems, vol. 2, pp. 92–95 (1992)Google Scholar
  2. 2.
    Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)CrossRefGoogle Scholar
  3. 3.
    Denisov, D.A., Dudkin, A.K.: Model-Based Chromosome Recognition via Hypotheses Construction/Verification. Pattern Recognition Letters 15(3), 299–307 (1994)zbMATHCrossRefGoogle Scholar
  4. 4.
    Fairhurst, M.C., Abdel Wahab, H.M.S.: An Interactive Two-Level Architecture for a Memory Network Pattern Classifier. Pattern Recognition Letters 11(8), 537–540 (1990)zbMATHCrossRefGoogle Scholar
  5. 5.
    Feder: Fractals. Plenum Press, New York (1988)zbMATHGoogle Scholar
  6. 6.
    Kimura, F., Shridhar, M.: Handwritten Numerical Recognition Based on Multiple Algorithms. Pattern Recognition 24(10), 969–983 (1991)CrossRefGoogle Scholar
  7. 7.
    El-Shishini, H., Abdel-Mottaleb, M.S., El-Raey, M., Shoukry, A.: A Multistage Algorithm for Fast Classification of Patterns. Pattern Recognition Letters 10(4), 211–215 (1989)CrossRefGoogle Scholar
  8. 8.
    Kurzynski, M.W.: On the Identity of Optimal Strategies for Multistage Classifiers. Pattern Recognition Letters 10(1), 39–46 (1989)zbMATHCrossRefGoogle Scholar
  9. 9.
    Zhou, J.Y., Pavlidis, T.: Discrimination of Characters by a Multi-Stage Recognition Process. Pattern Recognition 27(11), 1539-1549 (1994)CrossRefGoogle Scholar
  10. 10.
    Hashem, S., Schmeiser, B.: Improving Model Accuracy Using Optimal Linear Combinations of Trained Neural Networks. IEEE Trans. Neural Networks 6(3), 792–794 (1995)CrossRefGoogle Scholar
  11. 11.
    Lahiri, T., Samal, S.: A novel technique for making multiple classifier based decision. In: Proc. WSEAS International Conference on Mathematical Biology and Ecology, Corfu, Greece, August 17-19 (2004)Google Scholar
  12. 12.
    Jain, A.K., Prabhakar, S., Hong, L., Pankanti, S.: Filterbank-Based Fingerprint Matching. IEEE Transaction on Image Processing 9(5) (May 2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Rohit Singh
    • 1
  • Sandeep Samal
    • 2
  • Tapobrata Lahiri
    • 3
  1. 1.Wipro TechnologiesBangaloreIndia
  2. 2.Tata Consultancy ServicesBangalore
  3. 3.Indian Institute of Information TechnologyAllahabadIndia

Personalised recommendations