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Original Articles

Cost-sensitive incremental Classification under the MapReduce framework for Mining Imbalanced Massive Data Streams

Pages 177-194 | Received 01 Jun 2014, Published online: 07 Apr 2015

References

  • Dayrelis Mena-Torres, Jesús S. Aguilar-Ruiz. A similarity-based approach for data stream classification Expert Systems with Applications, Volume 41, Issue 9, July 2014, Pages 4224–4234
  • Peng Zhang, Byron J. Gao, Ping Liu, Yong Shi, Li Guo. A framework for application-driven classification of data streams. Neurocomputing, Volume 92, 1 September 2012, Pages 170–182.
  • Jing LIU, Guo-sheng XU, Shi-hui ZHENG, Da XIAO, Li-ze GU. Data streams classification with ensemble model based on decision-feedback. The Journal of China Universities of Posts and Telecommunications, Volume 21, Issue 1, February 2014, Pages 79–85.
  • Ammar Shaker, Robin Senge, Eyke Hüllermeier. Evolving fuzzy pattern trees for binary classification on data streams. Information Sciences, Volume 220, 20 January 2013, Pages 34–45.
  • Sungbo Seo, Jaewoo Kang, Keun Ho Ryu. Multivariable stream data classification using motifs and their temporal relations. Information Sciences, Volume 179, Issue 20, 29 September 2009, Pages 3489–3504.
  • Dewan Md. Farid, Li Zhang, Alamgir Hossain, Chowdhury Mofizur Rahman, Rebecca Strachan, Graham Sexton, Keshav Dahal. An adaptive ensemble classifier for mining concept drifting data streams. Expert Systems with Applications, Volume 40, Issue 15, 1 November 2013, Pages 5895–5906
  • Yanhong Li, Deyu Li, Suge Wang, Yanhui Zhai. Incremental entropy-based clustering on categorical data streams with concept drift. Knowledge-Based Systems. 2014, 59, pp: 33–47
  • Adel Ghazikhani, Reza Monsefi, Hadi Sadoghi Yazdi. Recursive least square perceptron model for non-stationary and imbalanced data stream classification. Evolving Systems. June 2013, Volume 4, Issue 2, pp: 119–131
  • Ditzler G, Polikar R. An ensemble based incremental learning framework for concept drift and class imbalance. Neural Networks (IJCNN), the 2010 International Joint Conference on.
  • Ke Wu, Andrea Edwards, Wei Fan, Jing Gao, Kun Zhang: Classifying Imbalanced Data Streams via Dynamic Feature Group Weighting with Importance Sampling. SDM 2014, pp: 722–730
  • Nguyen H.M., Cooper E.W., Kamei K. Online learning from imbalanced data streams. Soft Computing and Pattern Recognition (SoCPaR), 14-16 Oct. 2011, Page(s):347–352
  • Sheng Chen, Hoboken, NJ, USA; Haibo He. SERA: Selectively recursive approach towards nonstationary imbalanced stream data mining. Neural Networks, 2009. IJCNN 2009. International Joint Conference on,14-19 June 2009, pp:522–529
  • Thai-Nghe, N. Gantner, Z.; Schmidt-Thieme, L. Cost-sensitive learning methods for imbalanced data, Neural Networks (IJCNN), The 2010 International Joint Conference on .18-23 July 2010, pp:1–8
  • Adel Ghazikhani, Reza Monsefi, Hadi Sadoghi Yazdi. Online cost-sensitive neural network classifiers for non-stationary and imbalanced data streams. Neural Comput & Applic (2013),23:1283–1295
  • Liangxiao Jiang, Chaoqun Li, Shasha Wang. Cost-sensitive Bayesian network classifiers. Pattern Recognition Letters, Volume 45, 1 August 2014, Pages 211–216
  • Victoria López, Sara del Río, José Manuel Benítez, Francisco Herrera. Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data. Fuzzy Sets and Systems, In Press, Corrected Proof, Available online 24 February 2014.
  • Gao J, Ding B L, Fan W, Han J W and Philip S. Y. Classifying data streams with skewed class distributions and concept drifts. IEEE Internet Computing, 2008,12(6):37–49.
  • Adel G, Reza M and Hadi S Y. Ensemble of online neural networks for non-stationary and imbalanced data streams Neurocomputing, 25 December 2013, 122: 535–544.
  • Liu P, Wang Y, Cai L J and Zhang L B. Classifying skewed data streams based on reusing data. In Computer Application and System Modeling (ICCASM) of 2010 International Conference, 2010, 4:86—93.

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