Abstract
A fuzzy-rough set model is presented based on the extension of the classical rough set theory. The continuous attributes are fuzzified. The indiscernibility relation in classical rough set is extended to the fuzzy similarity relation. Then an inductive learning algorithm based on fuzzy-rough set model (FRILA) is proposed. Finally, with comparison to the decision tree algorithms, the effectiveness of the proposed method is verified by an example.
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© 2005 International Federation for Information Processing
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Hong, J., Lu, J., Shi, F. (2005). Fuzzy and Rough Set. In: Shi, Z., He, Q. (eds) Intelligent Information Processing II. IIP 2004. IFIP International Federation for Information Processing, vol 163. Springer, Boston, MA. https://doi.org/10.1007/0-387-23152-8_18
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DOI: https://doi.org/10.1007/0-387-23152-8_18
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-23151-8
Online ISBN: 978-0-387-23152-5
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