Abstract
Dimension reduction of data is an important theme in the data processing and on the web to represent and manipulate higher dimensional data. Rough set is fundamental and useful to process higher dimensional data. Reduct in the rough set is a minimal subset of features, which has the same discernible power as the entire features in the higher dimensional scheme. A nearest neighbor relation with minimal distance proposed here has a basic information for classification. In this paper, a new reduct generation method based on the nearest neighbor relation with minimal distance is proposed. To characterize the classification ability of reducts, we develop a graph mapping method of the nearest neighbor relation, which derives a higher classification accuracy.
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References
Pawlak, Z.: Rough Sets. International Journal of Computer and Information Science 11, 341–356 (1982)
Pawlak, Z., Slowinski, R.: Rough Set Approach to Multi-attribute Decision Analysis. European Journal of Operations Research 72, 443–459 (1994)
Skowron, A., Rauszer, C.: The Discernibility Matrices and Functions in Information Systems. In: Intelligent Decision Support-Handbook of Application and Advances of Rough Sets Theory, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)
Skowron, A., Polkowski, L.: Decision Algorithms, A Survey of Rough Set Theoretic Methods. Fundamenta Informatica 30(3-4), 345–358 (1997)
Pawlak, Z., Skowron, A.: Rough sets and Boolean reasoning. Information Science 177, 41–73 (2007)
Cover, T.M., Hart, P.E.: Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)
Ishii, N., Morioka, Y., Bao, Y., Tanaka, H.: Control of Variables in Reducts - kNN Classification with Confidence. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) KES 2011, Part IV. LNCS, vol. 6884, pp. 98–107. Springer, Heidelberg (2011)
Ishii, N., Torii, I., Bao, Y., Tanaka, H.: Modified Reduct Nearest Neighbor Classification. In: ACIS-ICIS, pp. 310–315. IEEE Comp. Soc. (2012)
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Ishii, N., Torii, I., Iwata, K., Nakashima, T. (2014). Generation of Reducts Based on Nearest Neighbor Relation. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2014. IDEAL 2014. Lecture Notes in Computer Science, vol 8669. Springer, Cham. https://doi.org/10.1007/978-3-319-10840-7_3
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DOI: https://doi.org/10.1007/978-3-319-10840-7_3
Publisher Name: Springer, Cham
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