An introduction of the condition class space with continuous value discretization and rough set theory
✍ Scribed by Malcolm J. Beynon
- Publisher
- John Wiley and Sons
- Year
- 2005
- Tongue
- English
- Weight
- 513 KB
- Volume
- 21
- Category
- Article
- ISSN
- 0884-8173
No coin nor oath required. For personal study only.
✦ Synopsis
The granularity of an information system has an incumbent effect on the efficacy of the analysis from many machine learning algorithms. An information system contains a universe of objects characterized and categorized by condition and decision attributes. To manage the concomitant granularity, a level of continuous value discretization ~CVD! is often undertaken. In the case of the rough set theory ~RST! methodology for object classification, the granularity contributes to the grouping of objects into condition classes with the same condition attribute values. This article exposits the effect of a level of CVD on the subsequent condition classes constructed, with the introduction of the condition class space-the domain within which the condition classes exist. This domain elucidates the association of the condition classes to the related decision outcomes-reflecting the inexactness incumbent when a level of CVD is undertaken. A series of measures is defined that quantify this association. Throughout this study and without loss of generality, the findings are made through the RST methodology. This further offers a novel exposition of the relationship between all the condition attributes and the RST-related reducts ~subsets of condition attributes!.