A Survey on Fast Feature Subset Selection Algorithm

The rapid advance of computer technologies in data processing, collection and storage has provided un-paralleled opportunities to expand capabilities in production, services communication and research. However, a feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view. It finds the subset of features. There are two steps of FAST algorithm. First, features are divided into clusters by using graph theoretic clustering method. Second, select feature from clusters that are highly correlated to the target classes. We are comparing FAST algorithm with the some representative feature subset selection algorithm name as Fast correlation based filter, Relief-F, Correlation based feature selection, Consist and FOCUS-SF. The results are available on high-dimensional data, microarray, text data and image data. Experimental results show that our FAST algorithm implementation can run faster and obtain better-extracted features than other methods.