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Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 3/2007

01.08.2007 | Theoretical Advances

An analysis of how training data complexity affects the nearest neighbor classifiers

verfasst von: J. S. Sánchez, R. A. Mollineda, J. M. Sotoca

Erschienen in: Pattern Analysis and Applications | Ausgabe 3/2007

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Abstract

The k-nearest neighbors (k-NN) classifier is one of the most popular supervised classification methods. It is very simple, intuitive and accurate in a great variety of real-world domains. Nonetheless, despite its simplicity and effectiveness, practical use of this rule has been historically limited due to its high storage requirements and the computational costs involved. On the other hand, the performance of this classifier appears strongly sensitive to training data complexity. In this context, by means of several problem difficulty measures, we try to characterize the behavior of the k-NN rule when working under certain situations. More specifically, the present analysis focuses on the use of some data complexity measures to describe class overlapping, feature space dimensionality and class density, and discover their relation with the practical accuracy of this classifier.
Vorheriger Artikel Periodicity and stability issues of a chaotic pattern recognition neural network
Nächster Artikel Sparse least squares support vector training in the reduced empirical feature space

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Literatur
1.
Bernadó E, Ho T-K (2004) On classifier domain of competence. In: Proceedings of 17th international conference on pattern recognition, Cambridge, pp 136–139
2.
Beyer KS, Goldstein J, Ramakrishnan R, Shaft U (1999) When is “nearest neighbor” meaningful? In: Proceedings of 7th international conference on database theory, Jerusalem, pp 217–235
3.
Cover TM (1968) Estimation by the nearest neighbor rule. IEEE Trans Inf Theory 14:50–55MATHCrossRef
4.
Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13:21–27MATHCrossRef
5.
Dasarathy BV (1991) Nearest neighbor norms: NN pattern classification techniques. IEEE Computer Society Press, Los Alamos
6.
Devijver PA, Kittler J (1992) Pattern recognition: a statistical approach. Prentice Hall, Englewood Cliffs
7.
Friedman JH (1997) On bias, variance, 0/1-loss, and the curse of dimensionality. Data Mining Knowl Discov 1:55–77CrossRef
8.
Gama J, Brazdil P (1995) Characterization of classification algorithms. In: Progress in artificial intelligence. Springer, Heidelberg, pp 83–102
9.
Hand DJ, Vinciotti V (2003) Choosing k for two-class nearest neighbour classifiers with unbalanced classes. Pattern Recognit Lett 24:1555–1562MATHCrossRef
10.
Ho T-K, Basu M (2002) Complexity measures of supervised classification problems. IEEE Trans Pattern Anal Mach Intell 24:289–300CrossRef
11.
Hoekstra A, Duin RPW (1996) On the nonlinearity of pattern classifiers. In: Proceedings of 13th international conference on pattern recognition, Vienna, pp 271–275
12.
Jain AK, Xu X, Ho T-K, Xiao F (2002) Uniformity testing using minimal spanning tree. In: Proceedings of 16th international conference on pattern recognition, Quebec City, pp 281–284
13.
Japkowicz N, Stephen S (2002) The class imbalance problem: a systematic study. Intell Data Anal 6:429–449MATH
14.
Kubat M, Chen W-K (1998) Weighted projection in nearest-neighbor classifiers. In: Proceedings of 1st Southern symposium on computing, Hattiesburg, pp 27–34
15.
Little RJA, Rubin DB (2002) Statistical analysis with missing data. Wiley, New YorkMATH
16.
Mollineda RA, Sánchez JS, Sotoca JM (2005) Data characterization for effective prototype selection. In: Proceedings of 2nd Iberian conference on pattern recognition and image analysis, Estoril, pp 27–34
17.
Okamoto S, Yugami N (2003) Effects of domain characteristics on instance-based learning algorithms. Theor Comput Sci 298:207–233MATHCrossRefMathSciNet
18.
Sánchez JS, Barandela R, Marqués AI, Alejo R, Badenas J (2003) Analysis of new techniques to obtain quality training sets. Pattern Recognit Lett 24:1015–1022CrossRef
19.
Singh S (2003) PRISM—a novel framework for pattern recognition. Pattern Anal Appl 6:134–149CrossRef
20.
Sohn S-Y (1999) Meta analysis of classification algorithms for pattern recognition. IEEE Trans Pattern Anal Mach Intell 21:1137–1144CrossRef
21.
Zhang J, Mani I (2003) kNN approach to unbalanced data distributions: a case study involving information extraction. In: Proceedings of workshop on learning from imbalanced datasets, Washington DC
22.
Wilson DL (1972) Asymptotic properties of nearest neighbor rules using edited data sets. IEEE Trans Syst Man Cybern 2:408–421MATHCrossRef
Metadaten
Titel
An analysis of how training data complexity affects the nearest neighbor classifiers
verfasst von
J. S. Sánchez
R. A. Mollineda
J. M. Sotoca
Publikationsdatum
01.08.2007
Verlag
Springer-Verlag
Erschienen in
Pattern Analysis and Applications / Ausgabe 3/2007
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-007-0061-2

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