Performance Comparison Based on Attribute Selection Tools for Data Mining
Recent years have been wide efforts in attribute selection research. Attribute selection can efficiently reduce the hypothesis space by removing irrelevant and redundant attributes. Attribute reduction of an information system is a key problem in rough set theory and its applications. In this paper, the authors compare the performance of attribute selection using two technical tools namely WEKA 3.7 and ROSE2. Filter methods are used an alternative measure instead of the error rate to score a feature subset.