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emrev12/cd_nse_ml_ref.txt

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  1. @emrev11 emrev11 created this gist Mar 22, 2018.
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    [LFR] : Wang, H., Abraham, Z., 2015. Concept drift detection for stream- ing data. In: International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1–9.
    [DDM] : Gama, J., Medas, P., Castillo, G., Rodrigues, P., 2004. Learning with drift detection. In: Advances in artificial intelligence–SBIA 2004. Springer, pp. 286–295.
    [EDDM] : Baena-Garcıa, M., del Campo-A ́vila, J., Fidalgo, R., Bifet, A., Gavalda, R., Morales-Bueno, R., 2006. Early drift detection method. In: Fourth international workshop on knowledge discov- ery from data streams. Vol. 6. pp. 77–86.
    [ADWIN] : Bifet, A., Gavalda, R., 2007. Learning from time-changing data with adaptive windowing. In: SDM. Vol. 7. SIAM
    [Resampling] : Harel, M., Mannor, S., El-Yaniv, R., Crammer, K., 2014. Concept drift detection through resampling. In: Proceedings of the 31st Inter- national Conference on Machine Learning (ICML-14). pp. 1009– 1017.
    [OLINDDA] : Spinosa, E. J., de Leon F de Carvalho, A. P., Gama, J., 2007. Olindda: A cluster-based approach for detecting novelty and concept drift in data streams. In: Proceedings of the 2007 ACM symposium on Applied computing. ACM, pp. 448–452.
    [MINAS] : Faria, E. R., Gama, J., Carvalho, A. C., 2013. Novelty detection algorithm for data streams multi-class problems. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing. ACM, pp. 795–800.
    [DETECTNOD] : Hayat, M. Z., Hashemi, M. R., 2010. A dct based approach for detecting novelty and concept drift in data streams. In: International Conference of Soft Computing and Pattern Recognition (SoCPaR). IEEE, pp. 373–378.
    [Woo-ensemble] : Ryu, J. W., Kantardzic, M. M., Kim, M.-W., Khil, A. R., 2012. An efficient method of building an ensemble of classifiers in streaming data. In: Big data analytics. Springer, pp. 122–133.
    [ECSMiner] : Masud, M. M., Gao, J., Khan, L., Han, J., Thuraisingham, B., 2011. Classification and novel class detection in concept-drifting data streams under time constraints. IEEE TKDE 23 (6), 859–874.
    [GC3] : Sethi, T. S., Kantardzic, M., Hu, H., 2016b. A grid density based framework for classifying streaming data in the presence of concept drift. Journal of Intelligent Information Systems 46 (1), 179– 211.
    [CoC] : Lee, J., Magoules, F., 2012. Detection of concept drift for learn- ing from stream data. In: IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS). IEEE, pp. 241–245.
    [HDDDM] : Ditzler, G., Polikar, R., 2011. Hellinger distance based drift detection for nonstationary environments. In: IEEE Symposium on Com- putational Intelligence in Dynamic and Uncertain Environments (CIDUE). IEEE, pp. 41–48.
    [PCA-1] : Kuncheva, L. I., Faithfull, W. J., 2014. Pca feature extraction for change detection in multidimensional unlabeled data. IEEE Transactions on Neural Networks and Learning Systems 25 (1), 69–80.
    [PCA-2] : Qahtan, A. A., Alharbi, B., Wang, S., Zhang, X., 2015. A pca-based change detection framework for multidimensional data streams: Change detection in multidimensional data streams. In: Proc. of the 21th ACM SIGKDD ICKDDM. ACM, pp. 935–944.
    [A-distance]: D. Kifer, S. Ben-David, and J. Gehrke, “Detecting change in data streams,” in Proc. 30th Int. Conf. Very Large Data Bases, 2004, vol. 30, pp. 180–191.
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