Abstract
An implicit assumption of many learning algorithm is that all attributes in the attribute set are of the same importance. However, this assumption is unreasonable or practical. If attributes in the attribute set are considered of non-equal importance with respect to their own situation, then the model obtained from those attributes would be more realistic. This paper designed a user oriented reduction model based on the minimal set cover by seamlessly combining an order of attributes that describes user preferences. The accessibility and efficiency of this algorithm is shown by an example.
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Han, S., Yin, G. (2012). A Model of User-Oriented Reduct Construction Based on Minimal Set Cover. In: Xiang, Y., Pathan, M., Tao, X., Wang, H. (eds) Data and Knowledge Engineering. ICDKE 2012. Lecture Notes in Computer Science, vol 7696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34679-8_10
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DOI: https://doi.org/10.1007/978-3-642-34679-8_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34678-1
Online ISBN: 978-3-642-34679-8
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