A feature reduction framework based on rough set for biomedical data sets

Feature selection reduces a data set into a subset which also represents the entire data with less computational complexity and performance does not affect much. However, to extract such a subset is a nontrivial task, although there are a number of methods to handle this problem. In the near past an approach based on rough set has been used for feature selection. The dependency measure is one of the ways to find out the minimal feature subset, called Reducts, from the entire dataset. One of the mature areas of feature reduction is the techniques based on rough set theory, which totally depends on the concept of sets and mathematical formulas. We have conducted experiments using different publicly available datasets from UCI repository and real data sets developed from patient report. A framework is devised using different rough set based algorithms, it has been observed that after reduction of attributes our results improved in terms of time complexity while a negligible effect is seen on the other measures. We measured the performance of our framework using precision, recall, accuracy and F-measure.