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Erschienen in:
2018 | OriginalPaper | Buchkapitel
9. Feature Selection Approach for Rule-Based Knowledge Bases
verfasst von : Agnieszka Nowak-Brzezińska
Erschienen in: Advances in Feature Selection for Data and Pattern Recognition
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Abstract
The subject-matter of this study is knowledge representation in rule-based knowledge bases. The two following issues will be discussed herein: feature selection as a part of mining knowledge bases from a knowledge engineer’s perspective (it is usually aimed at completeness analysis, consistency of the knowledge base and detection of redundancy and unusual rules) as well as from a domain expert’s point of view (domain expert intends to explore the rules with regard to their optimization, improved interpretation and a view to improve the quality of knowledge recorded in the rules). In this sense, exploration of rules, in order to select the most important knowledge, is based, in a great extent, on the analysis of similarities across the rules and their clusters. Building the representatives for created clusters of rules bases on the analysis of the left-hand sides of this rules and then selection of the best descriptive once. Thus we may treat this approach as a feature selection process.