Abstract
Many specific applications for electric power data, such as load forecasting and fault diagnosis, need to consider data changes during a period of time, rather than one record, to determine their decision classes, because the class label of only one record is meaningless. Based on the above discussion, interval-valued rough set is introduced. From the algebra view, we define the related concepts and prove the properties for the interval-valued reduction based on dependency, and present the corresponding heuristic reduction algorithm. In order to make the algorithm to achieve better results in practical applications, approximate reduction is introduced. To evaluate the proposed algorithm, we experiment on six months’ operating data of one 600MW unit in some power plant. Experimental results show that the algorithm proposed in this article can maintain a high classification accuracy with the proper parameters, and the numbers of objects and attributes can both be greatly reduced.
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© 2014 Springer International Publishing Switzerland
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Xu, F., Bi, Z., Lei, J. (2014). Approximate Reduction for the Interval-Valued Decision Table. In: Miao, D., Pedrycz, W., Ślȩzak, D., Peters, G., Hu, Q., Wang, R. (eds) Rough Sets and Knowledge Technology. RSKT 2014. Lecture Notes in Computer Science(), vol 8818. Springer, Cham. https://doi.org/10.1007/978-3-319-11740-9_9
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DOI: https://doi.org/10.1007/978-3-319-11740-9_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11739-3
Online ISBN: 978-3-319-11740-9
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