Feature Subset Selection Algorithms for Irrelevant Removal Using Minimum Spanning Tree Construction  
  Authors : Asifa Akthar Shaik; M.Purushottam


Feature selection in clustering is used for extracting the relevant data from a large collection of data by analyzing on various patterns of similar data. Based on the accuracy and efficiency of the data, major issue occurs in clustering. Feature selection may remedy this issue and thus enhance the prediction accuracy and minimize the particular computational overhead associated with classification algorithms. Irrelevant features usually do not contribute to the actual predictive accuracy, and also redundant features usually do not contribute in order to obtaining a better predictor with the they provide mostly information which can be already contained in other feature(s).


Published In : IJCAT Journal Volume 1, Issue 10

Date of Publication : 30 November 2014

Pages : 525 - 529

Figures :02

Tables : --

Publication Link :Feature Subset Selection Algorithms for Irrelevant Removal Using Minimum Spanning Tree Construction




Asifa Akthar Shaik : M.Tech 2nd Year, Department of CSE, SEAT, Tirupati, AP, India

M.Purushottam : Assistant Professor, Department of CSE, SEAT, Tirupati, AP, India










Symmetric uncertainty




Feature selection method is an efficient way to improve the accuracy of classifiers, dimensionality reduction, removing both irrelevant and redundant data. Thus SWIFT algorithm selects only fewer and relevant features which adds to the classifier accuracy when compared with PCA as shown in table 1. For the future work, we plan to explore different types of correlation measures, and study some formal properties of feature space.










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