Optimization of feature selection method for high dimensional data using fisher score and minimum spanning tree

For classification of High Dimensional data, feature selection is the most important step for obtaining optimal result with respect to processing power required and time taken. Feature selection is a method by which the most relevant feature is selected from a set of features containing redundant and irrelevant features thereby reducing the load on the classification algorithm. This paper proposes an implementation of this method in a two tier structure. In the first step, a high ranking feature is selected using the well-known filter based algorithm - Fisher Score. This algorithm selects the relevant feature from the feature set based on a preset threshold. The second step generates a cluster of redundant features utilizing MST (Minimum Spanning Tree) algorithm, which are then filtered out to preserve the most relevant features out of each cluster. This increases the classification accuracy as well as running time and hence the computation cost. The efficacy of the presented approach was validated by comparing the same with other well-known feature selection algorithms: Fisher Score, CFS and ConsSF with respect to three classifiers: IB1, C4.5, and Naïve Bayes. The presented approach achieved higher Classification accuracy and lower run time.