Abstract
Data uncertainty due to various causes, including imprecise measurement, network latency, out-dated sources and sampling errors, is common in real-world applications. Data Analysis applications are typical in collecting and accumulating large amounts of uncertain data. This attracted more and more database community to analyze and resolve the uncertainty incured in the large data sets. We, in this article, present a naive classifier, which is a Set-Valued counterpart of Naive Bayes that is extended to a general and flexible treatment of incomplete data, yielding to a new classifier called Naïve Credal Classifier. Naïve Credal Classifieris an application on closed and convex sets of probability distributions called Credal sets, of uncertainty measures. The Naïve Credal Classifier extends the discrete Naive Bayes classifier to imprecise probabilities and also models both prior ignorance and ignorance about the likelihood by sets of probability distributions. This is a new means to deal with uncertain data sets that departs significantly from most established conventional classification methods. Experimental results show that proposed model exhibits reasonable accuracy performance in classification on uncertain data.
An Erratum for this chapter can be found at http://dx.doi.org/10.1007/978-3-642-35314-7_87
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-642-35314-7_87
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Leung, C.K.-S.: Mining uncertain data. In: WIREs Data Mining and Knowledge Discovery, vol. 1. John Wiley & Sons, Inc. (July/August 2011)
Suresh, G.V., Shaik, S., Reddy, E.V., Shaik, U.A.: Gaussian Process Model for Uncertain Data Classification. International Journal of Computer Science and Information Security (IJCSIS) 8(9), 111–115 (2010)
Chau, M., Cheng, R., Kao, B.: Uncertain Data Mining: A New Research Direction. In: Proceedings of the Workshop on the Sciences of the Artificial, Hualien, Taiwan, December 7-8 (2005)
Aggarwal, C.C.: Managing and Mining Uncertain Data. Kluwer Academic Publishers, Boston
Aggarwal, C.C.: A Survey of Uncertain Data Algorithms and Applications. IEEE Transactions on Knowledge and Data Engineering 21(5) (2009)
Duda, R.O., Hart, P.E.: Pattern classification and scene analysis. Wiley, New York (1973)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification, 2nd edn. Wiley (2001)
Zaffalon, M.: A credal approach to naive classification. In: de Cooman, G., Cozman, F., Moral, S., Walley, P. (eds.) ISIPTA 1999, pp. 405–414. Univ. of Gent, Belgium (1999); The Imprecise Probabilities Project
Zaffalon, M.: Statistical inference of the naive credal classifier. In: de Cooman, G., Fine, T., Seidenfeld, T. (eds.) ISIPTA 2001, pp. 384–393. Shaker Publishing, The Netherlands (2001)
Walley, P.: Statistical Reasoning with Imprecise Probabilities. Chapman and Hall, New York (1991)
Walley, P.: Inferences from multinomial data: learning about a bag of marbles. J. R. Statist. Soc. B 58(1), 3–57 (1996)
Zaffalon, M., Wesnes, K., Petrini, O.: Reliable diagnoses of dementia by the naive credal classifier inferred from incomplete cognitive data. In: IDSIA, Galleria 2, 6928 Manno (Lugano), Switzerland
Zaffalon, M.: The naive credal classifier. Journal of Statistical Planning and Inference 105(1), 5–21 (2002)
Zaffalon, M.: Statistical inference of the naive credal classifier. In: de Cooman, G., Fine, T.L. (eds.) Proceedings of the Second International Symposium on Imprecise Probabilities and Their Applications, ISIPTA 2001, pp. 384–393. Shaker, The Netherlands (2001)
Zaffalon, M., Wesnes, K., Petrini, O.: Reliable diagnoses of dementia by the naive credal classifier inferred from incomplete cognitive data. Artificial Intelligence in Medicine 29(1-2), 61–79 (2003)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo
Wolpert, D.H., Wolf, D.R.: Estimating functions of probability distributions from a finite set of samples. Physical Review E 52(6), 6841–6854 (1995)
Zaffalon, M., Hutter, M.: Robust inference of trees, Tech. Rep. IDSIA-11-03, IDSIA (2003)
Murphy, P.M., Aha, D.W.: UCI repository of machine learning databases (1995), http://www.sgi.com/Technology/mlc/db/
Kohavi, R., John, G., Long, R., Manley, D., Pfleger, K.: MLC++: a machine learning library in C++. In: Tools with Artificial Intelligence, pp. 740–743. IEEE Computer Society Press (1994)
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Reddy, S.S.S., Suresh, G.V., Reddy, T.R., Vardhan, B.V. (2013). Retracted: Naive Credal Classifier for Uncertain Data Classification. In: Satapathy, S., Udgata, S., Biswal, B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA). Advances in Intelligent Systems and Computing, vol 199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35314-7_15
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DOI: https://doi.org/10.1007/978-3-642-35314-7_15
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