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