Supervised feature ranking approach based on probability density interval
Yaping Li · Jisuanji gongcheng yu sheji · 2009
In order to reduce dimensionality and improve efficiency, a novel supervised feature ranking approach based on probability density interval is proposed. First one of the dataset’s feature is weighted, then calculate the probability density interval between classes. The most important feature will result the biggest distances between classes. Therefore, probability density interval of inter-class to rank feature is used. Several experimental results demonstrate the effectiveness and the advantage of our approach here over Relief-F.