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

[1] Lakhpat Meena and V. Susheela Devi, Prototype Selection on Large and Streaming Data, International Conference on Neural Information Processing (ICONIP 2015), 2015.10.1007/978-3-319-26532-2_74Search 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, 1967.10.1109/TIT.1967.1053964Search in Google Scholar

[4] P.E. Hart, The condensed nearest neighbor rule. IEEE Trans. on Information Theory, IT-14(3): 515-516, 1968.10.1109/TIT.1968.1054155Search in Google Scholar

[5] G.W. Gates, The reduced nearest neighbour rule, IEEE Trans. on Information Theory, IT-18 (3): 431-433, 197210.1109/TIT.1972.1054809Search in Google Scholar

[6] V. Susheela Devi, M. Narasimha Murty. An incremental prototype set building technique, Pattern Recognition, 35: 505-513, 2002.10.1016/S0031-3203(00)00184-9Search in Google Scholar

[7] F. Angiulli, Fast Condensed Nearest Neighbor Rule, Proc. 22nd International Conf. Machine Learning (ICML ’05), 200510.1145/1102351.1102355Search 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, 2007.10.1109/TKDE.2007.190665Search 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, 2003.10.1109/TNN.2002.80431518237996Search 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, 200710.3233/IDA-2007-11604Search in Google Scholar

[12] K. Tabata, Maiko Sato, Mineichi Kudo, Data compression by volume prototypes for streaming data, Pattern Recognition, 43: 3162-3176, 201010.1016/j.patcog.2010.03.012Search 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, 2012.10.1109/TPAMI.2011.14221768651Search 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, 2014.10.1016/j.procs.2014.08.120Search in Google Scholar

[15] Jacob Bien, Robert Tibshirani, Prototype selection for interpretable classification, Annals of Applied Statistics, Vol. 5, No. 4, 2403-2424, 2011.10.1214/11-AOAS495Search 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, 2013.10.1016/j.patcog.2013.03.004Search 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