1. bookVolume 7 (2017): Issue 3 (July 2017)
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30 Dec 2014
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access type Open Access

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

Online veröffentlicht: 20 Mar 2017
Seitenbereich: 155 - 169
Eingereicht: 01 Jan 2016
Akzeptiert: 04 Jul 2016
Zeitschriftendaten
License
Format
Zeitschrift
Erstveröffentlichung
30 Dec 2014
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch

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.

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

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

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

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

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

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

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

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

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

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

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

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

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