International Journal of Engineering Associates
Feature selection has been one of the most important aspects of data mining. Every data set has a huge amount of in complete or unwanted data. This data have to be filtered before starting any data mining process. That where feature selections come in play. Various Feature selection algorithms are introduced till now and a lot of research has been done on it. In this paper, the authors introduce a new concept based on Correlation Feature Selection (CFS) called as Stratified sampling feature selection. The experiments were carried out on two data sets and finally comparison of both algorithms on those dataset is given.