Abstract
In the online approach a classifier is, as usual, induced from the available training set. However, in addition, there is also some adaptation mechanism providing for a classifier evolution after the classification task has been initiated and started. In this paper two algorithms for online learning and classification are considered. These algorithms work in rounds, where at each round a new instance is given and the algorithm makes a prediction. After the true class of the instance is revealed, the learning algorithm updates its internal hypothesis. Both algorithms are based on fuzzy C-means clustering followed by calculation of distances between cluster centroids and the incoming instance for which the class label is to be predicted. The proposed approach is validated experimentally. Experiment results show that both proposed classifiers can be considered as a useful extension of the existing range of online classifiers.
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References
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California. School of Information and Computer Science (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Bouchachia, A., Mittermeir, R.: Towards incremental fuzzy classifiers. Soft Computing 11, 193–207 (2007)
Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., Singer, Y.: Online passive–aggressive algorithms. Journal of Machine Learning Research 7, 551–585 (2006)
Domingos, P., Hulten, G.: A General Framework for Mining Massive Data Streams. Journal of Computational and Graphical Statistics 12, 1–6 (2003)
Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics 3, 32–57 (1973)
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery: An Overview. In: Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 1–30. AAAI/MIT Press (1996)
Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: Mining data streams: a review. ACM SIGMOD Record 34(1), 18–26 (2005)
Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: Data Stream Mining. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, Part 6, pp. 759–787 (2010)
Gama, J., Gaber, M.M.: Learning from Data Streams. Springer, Berlin (2007)
IDA Benchmark Repository, http://mldata.org/repository/tags/data/IDA_Benchmark_Repository/ (January 12, 2013)
Jędrzejowicz, J., Jędrzejowicz, P.: Cellular GEP-Induced Classifiers. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ICCCI 2010, Part I. LNCS (LNAI), vol. 6421, pp. 343–352. Springer, Heidelberg (2010)
Jędrzejowicz, J., Jędrzejowicz, P.: Rotation Forest with GEP-Induced Expression Trees. In: O’Shea, J., Nguyen, N.T., Crockett, K., Howlett, R.J., Jain, L.C. (eds.) KES-AMSTA 2011. LNCS (LNAI), vol. 6682, pp. 495–503. Springer, Heidelberg (2011)
Laskov, P., Gehl, C., Kruger, S., Muller, K.R.: Incremental support vector learning: analysis, implementations and applications. Machine Learning 7, 1909–1936 (2006)
Pramod, S., Vyas, O.P.: Data Stream Mining: A Review on Windowing Approach. Global Journal of Computer Science and Technology Software & Data Engineering 12(11), 26–30 (2012)
Shaker, A., Senge, R., Hllermeier, E.: Evolving fuzzy pattern trees for binary classification on data streams. Information Sciences 220, 34–45 (2013)
Wang, L., Ji, H.-B., Jin, Y.: Fuzzy Passive-Aggressive classification: A robust and efficient algorithm for online classification problems. Information Sciences 220, 46–63 (2013)
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Jędrzejowicz, J., Jędrzejowicz, P. (2013). Online Classifiers Based on Fuzzy C-means Clustering. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2013. Lecture Notes in Computer Science(), vol 8083. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40495-5_43
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DOI: https://doi.org/10.1007/978-3-642-40495-5_43
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