Subset Selection in High Dimensional Data by Using Fast Technique.

Feature subset selection is an effective way for reducing dimensionality, removing irrelevant data, increasing learning accuracy and improving results comprehensibility. This process improved by cluster based FAST Algorithm using MST construction. With the aim of choosing a subset of good features with respect to the target concepts, feature subset selection is an effective way for reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving result comprehensibility. Features in different clusters are relatively independent; the clustering-based strategy of FAST has a high probability of producing a subset of useful and independent features. The proposed algorithm not only reduces the number of features, but also improves the performances of the four well-known different types of classifiers such as the probability-based Naive Bayes, the tree-based C4.5, the instance-based IB1, and the rule-based RIPPER before and after feature selection. We can construct FAST algorithm with prim’s algorithm based on MST construction. Our experimental results show that improves the performances of the four types of classifiers.