Abstract
Prior to mining data for knowledge, selecting a potentially useful set of target data is necessary. Mining with missing attribute values increases uncertainty and decreases discovery accuracy. We present an instance selection method that determines the mining usability of an instance based on knowledge about which attributes are missing and the relative significance of the various attributes as defined by a domain expert. Knowledge-based instance selection (KbIS) is an instance utility metric that incorporates domain knowledge into a multi-criteria decision-making technique for instance selection.
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Wright, P., Hodges, J. (2001). KBIS: Using Domain Knowledge to Guide Instance Selection. In: Liu, H., Motoda, H. (eds) Instance Selection and Construction for Data Mining. The Springer International Series in Engineering and Computer Science, vol 608. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3359-4_15
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DOI: https://doi.org/10.1007/978-1-4757-3359-4_15
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