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Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

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Parallel MCNN (pMCNN) with Application to Prototype Selection on Large and Streaming Data

V. Susheela Devi / Lakhpat Meena
Published Online: 2017-03-20 | DOI: https://doi.org/10.1515/jaiscr-2017-0011


The Modified Condensed Nearest Neighbour (MCNN) algorithm for prototype selection is order-independent, unlike the Condensed Nearest Neighbour (CNN) algorithm. Though MCNN gives better performance, the time requirement is much higher than for CNN. To mitigate this, we propose a distributed approach called Parallel MCNN (pMCNN) which cuts down the time drastically while maintaining good performance. We have proposed two incremental algorithms using MCNN to carry out prototype selection on large and streaming data. The results of these algorithms using MCNN and pMCNN have been compared with an existing algorithm for streaming data.

Keywords: prototype selection; one-pass algorithm; streaming data; distributed algorithm


  • [1] Lakhpat Meena and V. Susheela Devi, Prototype Selection on Large and Streaming Data, International Conference on Neural Information Processing (ICONIP 2015), 2015.Google Scholar

  • [2] M. Narasimha Murty and V. Susheela Devi, Pattern Recognition: An Algorithmic Approach, Springer and Universities Press, 2012.Google Scholar

  • [3] T.M. Cover, P.E. Hart, Nearest neighbor pattern classification, IEEE Trans. on Information Theory, IT-13: 21-27, 1967.Google Scholar

  • [4] P.E. Hart, The condensed nearest neighbor rule. IEEE Trans. on Information Theory, IT-14(3): 515-516, 1968.Google Scholar

  • [5] G.W. Gates, The reduced nearest neighbour rule, IEEE Trans. on Information Theory, IT-18 (3): 431-433, 1972Google Scholar

  • [6] V. Susheela Devi, M. Narasimha Murty. An incremental prototype set building technique, Pattern Recognition, 35: 505-513, 2002.CrossrefGoogle Scholar

  • [7] F. Angiulli, Fast Condensed Nearest Neighbor Rule, Proc. 22nd International Conf. Machine Learning (ICML ’05), 2005Google Scholar

  • [8] Angiulli, Fabrizio, and Gianluigi Folino, Distributed nearest neighbor-based condensation of very large data sets, Knowledge and Data Engineering, IEEE Transactions on 19.12, 2007, 1593-1606, 2007.Google Scholar

  • [9] B. Karacali and H. Krim, Fast Minimization of Structural Risk by Nearest Neighbor Rule, IEEE Trans. Neural Networks, vol. 14, no. 1, pp. 127-134, 2003.Google Scholar

  • [10] Law, Yan-Nei and Zaniolo, Carlo, An adaptive nearest neighbor classification algorithm for data streams, In Knowledge Discovery in Databases: PKDD 2005, pp. 108120, Springer, 2005.Google Scholar

  • [11] J. Beringer, E. Hüllermeier, Efficient instance-based learning on data streams, Intelligent Data Analysis, 11 (6) 627-650, 2007Google Scholar

  • [12] K. Tabata, Maiko Sato, Mineichi Kudo, Data compression by volume prototypes for streaming data, Pattern Recognition, 43: 3162-3176, 2010Web of ScienceCrossrefGoogle Scholar

  • [13] Salvador Garcia, Joaquin Derrac, Prototype selection for nearest neighbor classification: Taxonomy and Empirical study, IEEE Trans. on PAMI, 34: 417-435, 2012.Google Scholar

  • [14] Ireneusz Czarnowski, Piotr Jedrzejowicz, Ensemble classifier for mining data streams, 18th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems(KES 2014), Procedia Computer Science, 35: 397-406, 2014.Google Scholar

  • [15] Jacob Bien, Robert Tibshirani, Prototype selection for interpretable classification, Annals of Applied Statistics, Vol. 5, No. 4, 2403-2424, 2011.CrossrefGoogle Scholar

  • [16] Shikha V. Gadodiya, Manoj B. Chandak, Prototype selection algorithms for kNN Classifier: A Survey, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), Vol. 2, Issue 12, pp. 4829-4832, 2013.Google Scholar

  • [17] Nele Verbiest, Chris Cornelis, Francisco Herrera, FRPS: A fuzzy rough prototype selection method, Vol. 46, Issue 10, 2770-2782, 2013.Google Scholar

  • [18] Juan Li, Yuping Wang, A nearest prototype selection algorithm using multi-objective optimization and partition, 9th International Conference on Computational Intelligence and Security, 264-268, 2013.Google Scholar

About the article

Received: 2016-01-01

Accepted: 2016-07-04

Published Online: 2017-03-20

Published in Print: 2017-07-01

Citation Information: Journal of Artificial Intelligence and Soft Computing Research, Volume 7, Issue 3, Pages 155–169, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2017-0011.

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© 2017 Academy of Management (SWSPiZ), Lodz. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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