References: [1] Mena-Torres., Dayrelis., Aguilar-Ruiz., Jesús, S. (2014). A similarity-based approach for data stream classification, Expert Systems with Applications, 41, p 4224–4234, 2014. [2] Cesare Alippi., Derong Liu., Dongbin Zhao., Li Bu. (2013). Detecting and Reacting to Changes in Sensing Units: The Active Classifier Case, IEEE Transactions on systems, man, and cybernetics: systems, 44 (3), 353 - 362. [3] Zhang, Peng., Zhou, Chuan., Wang, Peng., Gao, Byron., Zhu, Xingquan., Guo, Li. (2015). E-Tree: An Efficient Indexing Structure for Ensemble Models on Data Streams, IEEE transactions on knowledge and data engineering, 27, 2, p 461-474, February 2015. [4] Rutkowski, Leszek., Jaworski, Maciej., Pietruczuk, Lena., Duda, Piotr. (2014). Decision Trees for Mining Data Streams Based on the Gaussian Approximation, IEEE Transactions on Knowledge and Data Engineering, 26 (1), 108-119, January 2014. [5] Brzezinski, Dariusz., Stefanowski, Jerzy. (2014). Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm, IEEE transactions on neural networks and learning systems, 25 (1), 81-94, January 2014. [6] Gomes, João Bártolo., Gaber, Mohamed Medhat., Sousa, Pedro A. C., Menasalvas, Ernestina. (2014). Mining Recurring Concepts in a Dynamic Feature Space, IEEE Transactions on Neural Networks and Learning Systems, 25 (1), 95-110, January 2014. [7] Masud, Mohammad M., Chen, Qing., Khan, Latifur., Aggarwal, Charu, C., Gao, Jing., Han, Jiawei., Srivastava, Ashok ., and Oza, Nikunj, C. (2013). Classification and Adaptive Novel Class Detection of Feature-Evolving Data Streams, IEEE Transactions on Knowledge and Data Engineering, 25 (7), 1484-1497, July 2013. [8] Abdulsalam, Hanady., Skillicorn, David, B., Martin, Patrick. (2011). Classification Using Streaming Random Forests, IEEE Transactions on Knowledge and Data Engineering, 23 (1), 22-36, January 2011. [9] Fan, W. (2004). Systematic Data Selection to Mine Concept-Drifting Data Streams, In: Proceedings ACM SIGKDD 10th Int’l Conf. Knowledge Discovery and Data Mining, p. 128-137, 2004. [10]Gao, J., Fan, W., Han, J. (2007). On Appropriate Assumptions to Mine Data Streams, In: Proceedings IEEE Seventh Int’l Conf. Data Mining (ICDM), p 143-152, 2007. [11] Hulten, G., Spencer, L., Domingos, P. (2001). Mining Time-Changing Data Streams, In: Proceedings ACM SIGKDD Seventh Int’l Conf. Knowledge Discovery and Data Mining, p. 97-106. [12] Kolter, J., Maloof. M. (2005). Using Additive Expert Ensembles to Cope with Concept Drift, In: Proceedings 22nd Int’l Conf. Machine Learning (ICML) 449-456. [13] Wang, H., Fan, W., Yu, P.S., Han, J. (2003). Mining Concept-Drifting Data Streams Using Ensemble Classifiers, In: Proceedings ACM SIGKDD Ninth Int’l Conf. Knowledge Discovery and Data Mining, p. 226-235, 2003. [14] Bartolo Gomes, J., Menasalvas, E., Sousa, P. (2010). Tracking recurrent concepts using context,In: Proceedings 7th Int. Conf. RSCTC, 2010, p. 168–177. [15] Gama, J., Kosina, P. (2009). Tracking recurring concepts with metalearners, In: Proceedings 14th Portuguese Conf. Artif. Intell., October 2009, p. 423. [16] Katakis, I., Tsoumakas, G., Vlahavas, I. (2005). On the utility of incremental feature selection for the classification of textual data streams, In: Advances in Informatics. New York, NY, USA: Springer-Verlag, 2005, p 338–348 [17] Yang, Y., Wu, X., Zhu, X. (2006). Mining in anticipation for concept change: Proactive-reactive prediction in data streams, Data Mining Knowl. Discovery, 13 (3), 261–289, 2006. [18] Pawlak, Zdzisaw. (1982). Rough sets, International Journal of Parallel Programming, 11 (5), 341–356. [19] UC Irvine Machine Learning Repository from “http://archive.ics.uci.edu/ml/datasets.html”. [20] Ross, G. J., Tasoulis, D. K., Adams, N. M. (2012). Nonparametric monitoring of data streams for changes in location and scale, Technometr., 53(4), 379–389. [21] Zhu, X., Zhang, P., Lin, X., Shi, Y. (2010). Active Learning from Stream Data Using Optimal Weight Classifier Ensemble, IEEE Trans. System, Man, Cybernetics, Part B: Cybernetics, 40 (4), 1-15, December 2010. [22] Bifet, A., Holmes, G., Pfahringer, B., Kirkby, R., Gavalda, R. (2009). New Ensemble Methods for Evolving Data Streams, In: Proceedings 15th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD), 2009. [23] Masud, M., Gao, J., Khan, L., Han, J., Thuraisingham, B. (2011). Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints, IEEE Trans. Knowledge and Data Eng., 23 (6), 859-874, June 2011. [24] Wu, Shu., Wang, Shengrui . (2013). Information-Theoretic Outlier Detection for Large-Scale Categorical Data, IEEE Transactions on Knowledge and Data Engineering, 25 (3), March 2013. [25] Zadeh, L. (1992). Fuzzy sets. In: Fuzzy models for pattern recognition: Methods that search for structures in data, NY: IEEE Press, 1992. [26] MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations, In: Proceedings 5th Berkeley Symp. Math. Stat. Probab., L. M. L. Cam and J. Neyman, Eds. Berkeley, CA: Univ. California Press, 1967, vol. 1. |