Rapid Evaluation Based Feature Diminution algorithm

摘要

Rapid Evaluation-Based Feature Diminution algorithm involves finding a reduced set with the essential Feature which produces as the original set of Feature. The Feature diminution is performed by removing the irrelevant and redundant Features. The Rapid Evaluation-Based Feature Diminution algorithm (REFD) removes the irrelevancy by identifying relevant Feature to the target, and removing the rest and finally formed as weight graph. The weighted graph is converted to MST by Boruvka's algorithm. In the resultant set the representative features are selected by reducing MST into forest using Symmetric Uncertainty measures. The efficiency of REFD is improved by using efficient Minimum Spanning Tree (MST) clustering algorithm. The image, microarray and text dataset are taken as input to the REFD algorithm and reduced set given as the input to classifiers called the Naïve Bayes and the decision tree and compared with original set of Features. The REFD produces smaller subsets of Features yet increases the performances.