Classifier PGN: Classification with High Confidence Rules
ACM Computing Classification System (1998): H.2.8, H.3.3. Associative classifiers use a set of class association rules, generated from a given training set, to classify new instances. Typically, these techniques set a minimal support to make a first selection of appropriate rules and discriminate subsequently between high and low quality rules by means of a quality measure such as confidence. As a result, the final set of class association rules have a support equal or greater than a...