(untitled) (International Journal of Engineering and Computer Science, 2014)
Feature selection involves recognizing a subset of the majority helpful features that produces attuned results as the unique set of features. Feature selection algorithm can be evaluated from mutually efficiency and effectiveness points of vision. FAST algorithm is Proposed and then experimentally evaluated in this paper. FAST algorithm mechanism considering two steps. In the primary step, features are separated into clusters by means of graph-theoretic clustering methods. In the subsequent step, the majority delegate feature that is robustly connected to target classes is chosen from each cluster form a subset of features. The Features in unusual clusters are relatively self-governing; the clustering-based approach of FAST has a elevated possibility of producing a subset of useful features. in the direction of guarantee to the efficiency of FAST, we implement the efficient minimum-spanning tree clustering technique. general experiments are approved to contrast FAST and some delegate feature selection algorithms, namely, FCBF, ReliefF, CFS, Consist, and FOCUS-SF, by admiration to four types of famous classifiers, specifically, the probability-based Naive Bayes, the tree-based C4.5, the instance-based IB1, and the rule-based RIPPER and following feature selection.