Anzeige
Erschienen in:
2010 | OriginalPaper | Buchkapitel
Is It Important Which Rough-Set-Based Classifier Extraction and Voting Criteria Are Applied Together?
verfasst von : Dominik Ślȩzak, Sebastian Widz
Erschienen in: Rough Sets and Current Trends in Computing
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
We propose a framework for experimental verification whether mechanisms of voting among rough-set-based classifiers and criteria for extracting those classifiers from data should follow analogous mathematical principles. Moreover, we show that some of types of criteria perform better for high-quality data while the others are useful rather for low-quality data. The framework is based on the principles of approximate attribute reduction and probabilistic extensions of rough-set-based approach to data analysis. The framework is not supposed to produce the best-ever classification results, unless it is extended by some additional parameters known from the literature. Instead, our major goal is to illustrate in a possibly simplistic way that it is worth unifying mathematical background for the stages of learning and applying rough-set-based classifiers.