Attribute extraction and classification using rough sets on a lymphoma dataset
作者： Kenneth Revett , Nizamettin Aydin
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摘要: In this paper, we describe a rough sets approach to classification and attribute extraction of small biomedical dataset. The dataset contains 148 entries with 19 attributes on patients that were suspected have lymphoma. Our primary goal was be able create set rules allow the prediction decision class based values relevant attributes. preliminary study indicated seven predictive in accuracy approximately 85%, high sensitivity specificity. addition promising results, provided means dimensionality reduction rule generation.
westminster.ac.uk参考文章(8)
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