Article

Prototype Selection Via Prototype Relevance

Authors:

J. Fco. Martínez-TrinidadAuthors Info & Claims

CIARP '08: Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications

Pages 153 - 160

https://doi.org/10.1007/978-3-540-85920-8_19

Published: 09 September 2008 Publication History

Abstract

In Pattern recognition, the supervised classifiers use a training set T for classifying new prototypes. In practice, not all information in T is useful for classification therefore it is necessary to discard irrelevant prototypes from T . This process is known as prototype selection, which is an important task for classifiers since through this process the time in the training and/or classification stages could be reduced. Several prototype selection methods have been proposed following the Nearest Neighbor ( NN ) rule; in this work, we propose a new prototype selection method based on the prototype relevance and border prototypes, which is faster (over large datasets) than the other tested prototype selection methods. We report experimental results showing the effectiveness of our method and compare accuracy and runtimes against other prototype selection methods.

References

[1]

Bezdek, J.C., Kuncheva, L.I.: Nearest prototype classifier designs: An experimental study. International Journal of Intelligent Systems 16(12), 1445-1473 (2001)

Google Scholar

[2]

Wilson, D.R., Martínez, T.R.: Reduction Techniques for Instance-Based Learning Algorithms. Machine Learning 38, 257-286 (2000)

Crossref

Google Scholar

[3]

Brighton, H., Mellish, C.: Advances in Instance Selection for Instance-Based Learning Algorithms. Data Mining and Knowledge Discovery 6, 153-172 (2002)

Crossref

Google Scholar

[4]

Cover, T., Hart, P.: Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory 13, 21-27 (1967)

Google Scholar

[5]

Quinlan, J.R.: C4.5 Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)

Crossref

Google Scholar

[6]

Mitchell, T.M.: Machine Learning. WCB McGraw-Hill, Boston (1997)

Crossref

Google Scholar

[7]

Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons, New York (2001)

Crossref

Google Scholar

[8]

Hart, P.E.: The Condensed Nearest Neighbor Rule. IEEE Transactions on Information Theory 14(3), 515-516 (1968)

Google Scholar

[9]

Wilson, D.L.: Asymptotic Properties of Nearest Neighbor Rules Using Edited Data. IEEE Transactions on Systems, Man, and Cybernetics 2(3), 408-421 (1972)

Google Scholar

[10]

Chien-Hsing, C., Bo-Han, K., Fu, C.: The Generalized Condensed Nearest Neighbor Rule as A Data Reduction Method. In: 18th International Conference on Pattern Recognition, vol. 2, pp. 556-559. IEEE press, Washington (2006)

Crossref

Google Scholar

[11]

Tomek, I.: An Experiment with the Edited Nearest-Neighbor Rule. IEEE Transactions on Systems, Man, and Cybernetics 6-6, 448-452 (1976)

Google Scholar

[12]

Devijver, P.A., Kittler, J.: On the edited nearest neighbor rule. In: 5th International Conference on Pattern Recognition. The Institute of Electrical and Electronics Engineers, pp. 72-80 (1980)

Google Scholar

[13]

Wilson, D.R., Martínez, T.R.: Improved Heterogeneous Distance Functions. Journal of Artificial Intelligence Research 6-1, 1-34 (1997)

Crossref

Google Scholar

[14]

UCI Machine Learning Repository, University of California, School of Information and Computer Science, Irvine CA, http://www.ics.uci.edu/~mlearn/MLRepository.html

Google Scholar

Cited By

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Dif NElberrichi Z(2018)A Multi-Verse Optimizer Approach for Instance Selection and Optimizing 1-NN AlgorithmInternational Journal of Strategic Information Technology and Applications10.4018/IJSITA.20180401039:2(35-49)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.4018/IJSITA.2018040103
Villuendas-Rey YRey-Benguría CCaballero-Mota YGarcía-Lorenzo M(2012)Nearest Prototype Classification of Special School Families Based on Hierarchical Compact Sets ClusteringAdvances in Artificial Intelligence – IBERAMIA 201210.1007/978-3-642-34654-5_67(662-671)Online publication date: 13-Nov-2012
https://dl.acm.org/doi/10.1007/978-3-642-34654-5_67
Dey DSolorio TMontes y Gómez MEscalante H(2011)Instance selection in text classification using the silhouette coefficient measureProceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I10.1007/978-3-642-25324-9_31(357-369)Online publication date: 26-Nov-2011
https://dl.acm.org/doi/10.1007/978-3-642-25324-9_31

Prototype Selection Via Prototype Relevance
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

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CIARP '08: Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications

September 2008

805 pages

ISBN:9783540859192

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 09 September 2008

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Dif NElberrichi Z(2018)A Multi-Verse Optimizer Approach for Instance Selection and Optimizing 1-NN AlgorithmInternational Journal of Strategic Information Technology and Applications10.4018/IJSITA.20180401039:2(35-49)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.4018/IJSITA.2018040103
Villuendas-Rey YRey-Benguría CCaballero-Mota YGarcía-Lorenzo M(2012)Nearest Prototype Classification of Special School Families Based on Hierarchical Compact Sets ClusteringAdvances in Artificial Intelligence – IBERAMIA 201210.1007/978-3-642-34654-5_67(662-671)Online publication date: 13-Nov-2012
https://dl.acm.org/doi/10.1007/978-3-642-34654-5_67
Dey DSolorio TMontes y Gómez MEscalante H(2011)Instance selection in text classification using the silhouette coefficient measureProceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I10.1007/978-3-642-25324-9_31(357-369)Online publication date: 26-Nov-2011
https://dl.acm.org/doi/10.1007/978-3-642-25324-9_31

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A new fast prototype selection method based on clustering

Accurate and fast prototype selection based on the notion of relevant and border prototypes

Prototype selection based on sequential search

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