Attribute extraction and classification using rough sets on a lymphoma dataset
Kenneth Revett, Nizamettin Aydın · WestminsterResearch (University of Westminster) · 2005
In this paper, we describe a rough sets approach to classification and attribute extraction of a small biomedical dataset.The dataset contains 148 entries with 19 attributes on patients that were suspected to have a lymphoma.Our primary goal was to be able to create a set of rules that allow the prediction of the decision class based on the values of relevant attributes.Our preliminary study of this dataset indicated that seven of the 19 attributes were predictive in this dataset.Our classification accuracy was approximately 85%, with a high sensitivity and specificity.In addition to the promising classification results, rough sets provided a means of dimensionality reduction and rule generation.