Abstract
Rough set theory provides approaches to the finding a reduct (informally, an identifying set of attributes) from a decision system or a training set. In this paper, an algorithm for finding multiple reducts is developed. The algorithm has been used to find the multi-reducts in data sets from UCI Machine Learning Repository. The experiments show that many databases in the real world have multiple reducts. Using the multi-reducts, multi-knowledge is defined and an approach for extraction is presented. It is shown that a robot with multi-knowledge has the ability to identify a changing environment. Multi-knowledge can be applied in many application areas in machine learning or data mining domain.
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Wu, Q., Bell, D. (2003). Multi-knowledge Extraction and Application. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_37
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DOI: https://doi.org/10.1007/3-540-39205-X_37
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