Abstract
The article is exploring the learnabilty issues of decision tables acquired from data within the frameworks of rough set and of variable precision rough set models. Measures of learning problem complexity and of learned table domain coverage are proposed. Several methods for enhancing the learnabilty of decision tables are discussed, including a new technique based on value reducts.
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Ziarko, W. (2004). On Learnability of Decision Tables. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_47
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DOI: https://doi.org/10.1007/978-3-540-25929-9_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22117-3
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