Abstract
The Data Credibility Analysis is a computer science domain aimed at discovering universal algorithms for identifying improper or unusual data. It is done by calculating credibility coefficients for individual records. In recent years many different methods of computing these coefficients were presented. In the paper we propose a transformation of credibility coefficients to ordinal credibility coefficients. By developing this idea we propose another credibility coefficient computing algorithm, which benefits from incorporating arbit rary many other credibility coefficient computing methods. The preliminary tests showed that this approach leads to better results.
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References
Podraza, R., Walkiewicz, M., Dominik, A.: Credibility Coefficients in ARES Rough Set Exploration System. In: Ślęzak, D., et al. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 29–38. Springer, Heidelberg (2005)
Podraza, R., Dominik, A.: Problem of Data Reliability in Decision Tables. Int. J. of Information Technology and Intelligent Computing (IT&IC) 1(1), 103–112 (2006)
Podraza, R., Walkiewicz, M., Dominik, A.: Credibility Coefficients Based on Frequent Sets. In: Conf. on Comp. Sci.- Research and Applications, Kazimierz Dolny, Poland, 2006. To be published in Annales UMCS, AI Informatica, Lublin, Poland (2006)
Podraza, R., Walkiewicz, M., Dominik, A.: Credibility Coefficients Based on Decision Rules. In: Proceedings of the Int. Multiconference on Comp. Sci. and Inf. Technology, vol. 1, XXII Autumn Meeting of Polish Information Processing Society, Wisła, Poland, pp. 179–187 (2006)
Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)
Dong, G., et al.: CAEP: Classification by Aggregating Emerging Patterns. In: Proc. of 2nd Int. Conf. on Discovery Science, Tokyo, Japan, pp. 30–42 (1999)
Podraza, R., Tomaszewski, K.: KTDA: Emerging Patterns Based Data Analysis System. In: Annales UMCS, Informatica, AI, vol.4, Lublin, Poland, pp. 279–290 (2006)
Dong, G., Li, J.: Efficient Mining of Emerging Patterns: Discovering Trends and Differences. In: Proc. of the SIGKDD (5th ACM Int. Conf. on Knowledge Discovery and Data Mining), San Diego, USA, pp. 43–52 (1999)
Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. University of California, Irvine (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
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Podraza, R., Tomaszewski, K. (2007). Ordinal Credibility Coefficient – A New Approach in the Data Credibility Analysis. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2007. Lecture Notes in Computer Science(), vol 4482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72530-5_22
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DOI: https://doi.org/10.1007/978-3-540-72530-5_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72529-9
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