By Johannes Fürnkranz
Rules – the clearest, such a lot explored and most sensible understood type of wisdom illustration – are quite vital for info mining, as they provide the simplest tradeoff among human and laptop understandability. This e-book offers the basics of rule studying as investigated in classical computing device studying and smooth information mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule studying, therefore bridging the distance among attribute-value studying and inductive good judgment programming, and delivering whole insurance of most crucial parts of rule learning.
The ebook can be utilized as a textbook for educating desktop studying, in addition to a finished connection with examine within the box of inductive rule studying. As such, it pursuits scholars, researchers and builders of rule studying algorithms, proposing the elemental rule studying ideas in adequate breadth and intensity to allow the reader to appreciate, enhance and follow rule studying thoughts to real-world data.
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Extra resources for Foundations of Rule Learning
Using a refinement operator, it is easy to define a simple general-to-specific search algorithm for learning individual rules. A possible implementation of this algorithm, called LEARNONERULE, is sketched in Fig. 8. The algorithm repeatedly refines the current best rule, and selects the best of all computed refinements according to some quality criterion. This amounts to a top-down hill-climbing2 2 If the term ‘top-down hill-climbing’ sounds contradictory: hill-climbing refers to the process of greedily moving towards a (local) optimum of the evaluation function, whereas top-down refers to the fact that the search space is searched by successively specializing the candidate rules, thereby moving downwards in the generalization hierarchy induced by the rules.
LEARNONERULE is, essentially, equivalent to the algorithm used in the PRISM learning system (Cendrowska, 1987). 3 This strategy is, for example, used in the CN2 learning algorithm. The LEARNONERULE algorithm contains several heuristic choices. For example, it uses a heuristic quality function for selecting the best refinement, and it stops rule refinement either when a stopping criterion is satisfied or when no further refinement is possible. We will briefly discuss these options in the next section, but refer to Chaps.
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Foundations of Rule Learning by Johannes Fürnkranz