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

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Abstract

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.

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References

  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.

    Article  Google Scholar 

  2. Cover T M, Hart P E. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, January 1967, 13: 21–27.

    Article  MATH  Google Scholar 

  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.

  4. Hart P. The condensed NN rule. IEEE Trans. Information Theory, May 1968, 14(3): 515–516.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  10. Friedman J. Flexible metric nearest neighbor classification. Tech. Rep.113, Stanford University, 1994.

  11. Hastie T, Tibshirani R. Discriminant adaptive nearest neighbor classification. IEEE Trans. PAMI, 1996, 18(6): 607–615.

    Google Scholar 

  12. Domeniconi C, Peng J, Gunopulos D. Locally adaptive metric nearest neighbor classification. IEEE Trans. PAMI, 2004, 24: 1281–1285.

    Google Scholar 

  13. Pedreira C. Learning vector quantization with training data selection. IEEE Trans. PAMI, 2006, 18(1): 157–162.

    MathSciNet  Google Scholar 

  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.

    Article  Google Scholar 

  15. Li S Z, Lu J. Face Recognition using the nearest feature line method. IEEE Trans. Neural Networks, 1999, 10(2): 439–443.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  MATH  Google Scholar 

  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.

    Article  MATH  Google Scholar 

  21. Zheng W, Zhao L, Zou C. Locally nearest neighbor classifiers for pattern classification. Pattern Recognition. 2004, 37(6): 1307–1309.

    Article  MATH  Google Scholar 

  22. Zhou Y, Zhang C, Wang J. Tunable nearest neighbour classifier. Lect. Notes Comput. Sci., 2004, 3175: 204–211.

    Google Scholar 

  23. Gao Q B, Wang Z Z. Center-based nearest neighbour classifier. Pattern Recognition, 2007, 40(1): 346–349.

    Article  MATH  Google Scholar 

  24. Bezdek J C. Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum, 1981.

    MATH  Google Scholar 

  25. Lumini A, Nanni L. A clustering method for automatic biometric template selection. Pattern Recognition, 2006, 39(3): 495–497.

    Article  MATH  Google Scholar 

  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.

  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.

  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.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  31. Hua S, Sun Z. Support vector machine approach for protein subcellular localization prediction. Bioinformatics, 2001, 17(8): 721–728.

    Article  Google Scholar 

  32. Demsar J. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 2006, 7: 1–30.

    MathSciNet  Google Scholar 

  33. Kuncheva L. Combining Pattern Classifiers. John Wiley & Sons, 2004.

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Authors and Affiliations

  1. DEIS, IEIIT-CNR, Università di Bologna, Viale Risorgimento 2, 40136, Bologna, Italy

    Loris Nanni & Alessandra Lumini

Authors
  1. Loris Nanni
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  2. Alessandra Lumini
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Correspondence to Loris Nanni.

Additional information

This work has been supported by the European Commission IST-2002-507634 Biosecure NoE Projects.

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Nanni, L., Lumini, A. Cluster-Based Nearest-Neighbour Classifier and Its Application on the Lightning Classification. J. Comput. Sci. Technol. 23, 573–581 (2008). https://doi.org/10.1007/s11390-008-9153-8

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  • DOI: https://doi.org/10.1007/s11390-008-9153-8

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