Abstract
In this paper we extend previous research and present a novel approach to construct multivariate decision tree, which has to some extent the ability of fault tolerance, by employing a development of RST, namely the variable precision rough sets (VPRS) model. Based on variable precision rough set theory, a new concept of generalization of one equivalence relation with respect to another one with precision β is introduced and used for construction of multivariate decision tree. The experimentation result shows its fitness to create multivariate decision tree retrieved from noisy data.
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Zhang, L., Ye, YM., Yu, S., Ma, FY. (2003). A New Multivariate Decision Tree Construction Algorithm Based on Variable Precision Rough Set. In: Dong, G., Tang, C., Wang, W. (eds) Advances in Web-Age Information Management. WAIM 2003. Lecture Notes in Computer Science, vol 2762. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45160-0_23
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DOI: https://doi.org/10.1007/978-3-540-45160-0_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40715-7
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