PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Parallel MCNN (PMCNN) with application to prototype selection on large and streaming data

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Strony
155--169
Opis fizyczny
Bibliiogr. 18 poz., rys.
Twórcy
autor
  • Department of Computer Science and Automation, Indian Institute of Science, Bangalore, India
autor
  • Department of Computer Science and Automation, Indian Institute of Science, Bangalore, India
Bibliografia
  • [1] Lakhpat Meena and V. Susheela Devi, Prototype Selection on Large and Streaming Data, International Conference on Neural Information Processing(ICONIP 2015), 2015.
  • [2] M. Narasimha Murty and V. Susheela Devi, Pattern Recognition: An Algorithmic Approach, Springer and Universities Press, 2012.
  • [3] T.M. Cover, P.E. Hart, Nearest neighbor pattern classification, IEEE Trans. on Information Theory, IT-13: 21-27, 1967.
  • [4] P.E. Hart, The condensed nearest neighbor rule. IEEE Trans. on Information Theory, IT-14(3): 515-516, 1968.
  • [5] G.W. Gates, The reduced nearest neighbour rule, IEEE Trans. on Information Theory, IT-18 (3): 431-433, 1972
  • [6] V. Susheela Devi, M. Narasimha Murty. An incremental prototype set building technique, Pattern Recognition, 35: 505-513, 2002.
  • [7] F. Angiulli, Fast Condensed Nearest Neighbor Rule, Proc. 22nd International Conf. Machine Learning (ICML ’05), 2005
  • [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.
  • [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] 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.
  • [11] J. Beringer, E. Hullermeier, Efficient instance- ¨based learning on data streams, Intelligent Data Analysis, 11 (6) 627-650, 2007
  • [12] K. Tabata, Maiko Sato, Mineichi Kudo, Data compression by volume prototypes for streaming data, Pattern Recognition, 43: 3162-3176, 2010
  • [13] Salvador Garcia, Joaquin Derrac, Prototype selection for nearest neighbor classification: Taxonomy and Empirical study, IEEE Trans. on PAMI, 34: 417-435, 2012.
  • [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.
  • [15] Jacob Bien, Robert Tibshirani, Prototype selection for interpretable classification, Annals of Applied Statistics, Vol. 5, No. 4, 2403-2424, 2011.
  • [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.
  • [17] Nele Verbiest, Chris Cornelis, Francisco Herrera, FRPS: A fuzzy rough prototype selection method, Vol. 46, Issue 10, 2770-2782, 2013.
  • [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.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ac419e7b-fd97-4c64-abc4-ee6dc64193d7
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.