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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Virtual Balancing of Decision Classes
verfasst von : Marzena Kryszkiewicz
Erschienen in: Intelligent Information and Database Systems
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Abstract
It has been observed in the literature and practice that the quality of classifying based on confidences of decision rules is poor when a decision table consists of decision classes which significantly differ in the number of objects. A typical approach to overcome negative consequences of the occurrence of this phenomenon is to apply oversampling of minority decision classes and/or undersampling of majority decision classes. In this paper, we introduce a notion of a virtual balancing of decision classes, which does not require any replication of data, but produces the same results as a physical balancing of decision classes. Also, we derive a number of properties of selected evaluation measures (coverage, confidence, lift and growth) of decision rules and relations among them w.r.t. virtually (and by this, physically) balanced decision classes. In particular, we show how to determine threshold values for confidence, lift, growth and coverage so that resulting sets of decision rules were identical.