Maximum Spanning Tree Based Redundancy Elimination for Feature Selection of High Dimensional Data

摘要

Feature selection adheres to the phenomena of preprocessing step for High Dimensional data to obtain optimal results with reference of speed and time. It is a technique by which most prominent features can be selected from a set of features that are prone to contain redundant and relevant features. It also helps to lighten the burden on classification techniques, thus makes it faster and efficient. We introduce a novel two tiered architecture of feature selection that can able to filter relevant as well as redundant features. Our approach utilizes the peculiar advantage of identifying highly correlated nodes in a tree. More specifically, the reduced dataset comprises of these selected features. Finally, the reduced dataset is tested with various classification techniques to evaluate their performance. To prove its correctness we have used many basic algorithms of classification to highlight the benefits of our approach. In this journey of work we have used benchmark datasets to prove the worthiness of our approach.