Abstract
This paper introduces the use of ERID [1] algorithm for classification rule discovery at various levels of granularity. We use an incomplete information system and attribute value hierarchy to extract rules. The incomplete information system is capable of storing weighted attribute values and the domains of those attributes are organized using a hierarchical tree structure. The granularity of attribute values can be adjusted using the attribute value hierarchy. The result is then processed through ERID, which is designed to discover rules from partially incomplete information systems. The capability of handling incomplete data enables to build more specific and general classification rules.
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Im, S., Raś, Z.W., Tsay, LS. (2008). Multi-granularity Classification Rule Discovery Using ERID. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2008. Lecture Notes in Computer Science(), vol 5009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79721-0_67
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DOI: https://doi.org/10.1007/978-3-540-79721-0_67
Publisher Name: Springer, Berlin, Heidelberg
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