Journal of Computer Science and Technology

, Volume 23, Issue 4, pp 573–581 | Cite as

Cluster-Based Nearest-Neighbour Classifier and Its Application on the Lightning Classification

  • Loris NanniEmail author
  • Alessandra Lumini
Regular Paper


The problem addressed in this paper concerns the prototype generation for a cluster-based nearest-neighbour classifier. It considers, to classify a test pattern, the lines that link the patterns of the training set and a set of prototypes. An efficient method based on clustering is here used for finding subgroups of similar patterns with centroid being used as prototype. A learning method is used for iteratively adjusting both position and local-metric of the prototypes. Finally, we show that a simple adaptive distance measure improves the performance of our nearest-neighbour-based classifier. The performance improvement with respect to other nearest-neighbour-based classifiers is validated by testing our method on a lightning classification task using data acquired from the Fast On-orbit Recording of Transient Events (FORTE) satellite, moreover the performance improvement is validated through experiments with several benchmark datasets. The performance of the proposed methods are also validated using the Wilcoxon Signed-Rank test.


nearest-neighbour classifier clustering adaptive distance 


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  1. [1]
    Parades R, Vidal E. Learning prototypes and distances: A prototype reduction technique based on nearest neighbor error minimization. Pattern Recognition, 2006, 39: 180–188.CrossRefGoogle Scholar
  2. [2]
    Cover T M, Hart P E. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, January 1967, 13: 21–27.zbMATHCrossRefGoogle Scholar
  3. [3]
    Franco A, Maltoni D, Nanni L. Reward-punishment editing. In Proc. International Conference on Pattern Recognition (ICPR04), Cambridge, UK, August 2004, pp.424–427.Google Scholar
  4. [4]
    Hart P. The condensed NN rule. IEEE Trans. Information Theory, May 1968, 14(3): 515–516.CrossRefGoogle Scholar
  5. [5]
    Zhu H, Basir O. An adaptive fuzzy evidential nearest neighbour formulation for classifying remote sensing images. IEEE Trans. Geosci. Remote Sens., Aug. 2005, 43(8): 1874–1889.CrossRefGoogle Scholar
  6. [6]
    Keller J M, Gray M R, Givens J A. A fuzzy k-nearest neighbour algorithm. IEEE Trans. Syst., Man, Cybern., 1995, 25(5): 804–813.CrossRefGoogle Scholar
  7. [7]
    Ghosh A K, Chaudhuri P, Murthy C A. On visualization and aggregation of nearest neighbor classifiers. IEEE Trans. Pattern Anal. Mach. Intell., Oct. 2005, 27(10): 1592–1602.CrossRefGoogle Scholar
  8. [8]
    Ghosh A K, Chaudhuri P, Murthy C A. Multiscale classification using nearest neighbor density estimates. IEEE Trans. Syst., Man, Cybern., 2006, 36(5): 1139–1148.CrossRefGoogle Scholar
  9. [9]
    Li B, Chen Y. The nearest neighbor algorithm of local probability centers. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 2008, 38(1): 141–154.CrossRefGoogle Scholar
  10. [10]
    Friedman J. Flexible metric nearest neighbor classification. Tech. Rep.113, Stanford University, 1994.Google Scholar
  11. [11]
    Hastie T, Tibshirani R. Discriminant adaptive nearest neighbor classification. IEEE Trans. PAMI, 1996, 18(6): 607–615.Google Scholar
  12. [12]
    Domeniconi C, Peng J, Gunopulos D. Locally adaptive metric nearest neighbor classification. IEEE Trans. PAMI, 2004, 24: 1281–1285.Google Scholar
  13. [13]
    Pedreira C. Learning vector quantization with training data selection. IEEE Trans. PAMI, 2006, 18(1): 157–162.MathSciNetGoogle Scholar
  14. [14]
    Wang J, Neskovic P, Cooper L N. Improving nearest neighbor rule with a simple adaptive distance measure. Pattern Recognition Letters, 2007, 28(2): 207–213.CrossRefGoogle Scholar
  15. [15]
    Li S Z, Lu J. Face Recognition using the nearest feature line method. IEEE Trans. Neural Networks, 1999, 10(2): 439–443.CrossRefGoogle Scholar
  16. [16]
    Chien J T, Wu C C. Discriminant waveletfaces and nearest feature classifiers for face recognition. IEEE Trans. Pattern Anal. Machine Intell., 2002, 24(12): 1644–1649.CrossRefGoogle Scholar
  17. [17]
    Li S Z. Content-based audio classification and retrieval using the nearest feature line method. IEEE Trans. Speech Audio Process., 2000, 8(5): 619–625.CrossRefGoogle Scholar
  18. [18]
    Li S Z, Chan K L, Wang C L. Performance evaluation of the nearest feature line method in image classification and retrieval. IEEE Trans. Pattern Anal. Machine Intell., 2000, 22(11): 1335–1339.CrossRefGoogle Scholar
  19. [19]
    Chen K, Wu T Y, Zhang H J. On the use of nearest feature line for speaker identification. Pattern Recognition Lett., 2002, 23(14): 1735–1746.zbMATHCrossRefGoogle Scholar
  20. [20]
    Chen J H, Chen C S. Object recognition based on image sequences by using inter-feature-line consistencies. Pattern Recognition, 2004, 37(9): 1913–1923.zbMATHCrossRefGoogle Scholar
  21. [21]
    Zheng W, Zhao L, Zou C. Locally nearest neighbor classifiers for pattern classification. Pattern Recognition. 2004, 37(6): 1307–1309.zbMATHCrossRefGoogle Scholar
  22. [22]
    Zhou Y, Zhang C, Wang J. Tunable nearest neighbour classifier. Lect. Notes Comput. Sci., 2004, 3175: 204–211.Google Scholar
  23. [23]
    Gao Q B, Wang Z Z. Center-based nearest neighbour classifier. Pattern Recognition, 2007, 40(1): 346–349.zbMATHCrossRefGoogle Scholar
  24. [24]
    Bezdek J C. Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum, 1981.zbMATHGoogle Scholar
  25. [25]
    Lumini A, Nanni L. A clustering method for automatic biometric template selection. Pattern Recognition, 2006, 39(3): 495–497.zbMATHCrossRefGoogle Scholar
  26. [26]
    Eads D, Hill D, Davisa S, Perkinsa S, Maa J, Portera R, Theiler J. Genetic algorithms and support vector machines for time series classification. In Proc. Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V, Seattle, USA, December 2002, pp.74–85.Google Scholar
  27. [27]
    Briles S, Moore K, Jones R, Blain P, Klinger P, Neagley D, Carey M, Henneke K, Spurgen W. Innovative use of DSP technology in space: FORTE event classifier. In Proc. the International Workshop on Artificial Intelligence in Solar-Terrestrial Physics, Dallas, USA, 1993.Google Scholar
  28. [28]
    Moore K, Blain P C, Briles S D, Jones R G. Classification of RF transients in space using digital signal processing and neural network techniques. In Proc. SPIE, 2492, Orlando, USA, 1997.Google Scholar
  29. [29]
    Wang J, Neskovic P, Cooper L N. Improving nearest neighbor rule with a simple adaptive distance measure. Pattern Recognition Letters, 2007, 28(2): 207–213.CrossRefGoogle Scholar
  30. [30]
    Rögnvaldsson T, You L. Why neural networks should not be used for HIV-1 protease cleavage site prediction. Bioinformatics, 2004, 20(11): 1702–1709.CrossRefGoogle Scholar
  31. [31]
    Hua S, Sun Z. Support vector machine approach for protein subcellular localization prediction. Bioinformatics, 2001, 17(8): 721–728.CrossRefGoogle Scholar
  32. [32]
    Demsar J. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 2006, 7: 1–30.MathSciNetGoogle Scholar
  33. [33]
    Kuncheva L. Combining Pattern Classifiers. John Wiley & Sons, 2004.Google Scholar

Copyright information

© Springer 2008

Authors and Affiliations

  1. 1.DEIS, IEIIT-CNRUniversità di BolognaBolognaItaly

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