Paper
27 March 2001 Statistical extension of rough set rule induction
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Abstract
Rough set based rule induction methods have been applied to knowledge discovery in databases. The empirical results obtained show that they are very powerful and that some important knowledge has been extracted from datasets. However, quantitative evaluation of induced rules are based not on statistical evidence but on rather naive indices, such as conditional probabilities and functions of conditional probabilities. In this paper, we introduce a new approach to induced rules for quantitative evaluation, which can be viewed as a statistical extension of rough set methods. For this extension, chi-square distribution and F- distribution play an important role in statistical evaluation.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shusaku Tsumoto "Statistical extension of rough set rule induction", Proc. SPIE 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, (27 March 2001); https://doi.org/10.1117/12.421072
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Cited by 6 scholarly publications.
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KEYWORDS
Statistical analysis

Distance measurement

Statistical modeling

Binary data

Data modeling

Knowledge discovery

Databases

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