Supervised Descriptive Rule Induction
Supervised descriptive rule induction (SDRI) is a machine learning task in which individual patterns in the form of rules (see Classification rule) intended for interpretation are induced from data, labeled by a predefined property of interest. In contrast to standard supervised rule induction, which aims at learning a set of rules defining a classification/prediction model, the goal of SDRI is to induce individual descriptive patterns. In this respect SDRI is similar to association rule discovery, but the consequents of the rules are restricted to a single variable – the property of interest – and, except for the discrete target attribute, the data is not necessarily assumed to be discrete.
Supervised descriptive rule induction assumes a set of training examples, described by attributes and their values and a selected attribute of interest (called the target attribute). Supervised descriptive rule induction induces rules that may each be interpreted...
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