Abstract:The requirements of real-world data mining problems vary extensively. It is plausible to assume that some of these requirements can be expressed as application-specific performance metrics. An algorithm that is designed to maximize performance given a certain learning metric may not produce the best possible result according to these applicationspecific metrics. We have implemented A Metric-based One Rule Inducer (AMORI), for which it is possible to select the learning metric. We have compared the performance … Show more
“…This metric, called the Candidate Evaluation Function (CEF), has the main purpose of combining an arbitrary number of individual metrics into a single quantity. In this study, we use a revised version [15] of CEF.…”
Section: Application-oriented Validation and Evaluationmentioning
“…This metric, called the Candidate Evaluation Function (CEF), has the main purpose of combining an arbitrary number of individual metrics into a single quantity. In this study, we use a revised version [15] of CEF.…”
Section: Application-oriented Validation and Evaluationmentioning
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