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Rough Set-Based Feature Subset Selection Technique Using Jaccard’s Similarity Index

  • Bhawna Tibrewal
  • Gargi Sur Chaudhury
  • Sanjay Chakraborty
  • Animesh Kairi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 811)

Abstract

Feature selection is the tool required to study data with high dimensions in an easy way. It involves extracting attributes from a dataset having a large number of attributes in such a way so as the reduced attribute set can describe the dataset in a manner similar to that of the entire attribute set. Reducing the features of the data and selecting only the more relevant features reduce the computational and storage requirements which are needed to process the entire dataset. Rough set is the approach of approximating a conventional set. It is used in data mining for reduction of datasets and to find hidden pattern in datasets. This paper aims to devise an algorithm which performs feature selection on a given dataset using the concepts of rough set.

Keywords

Feature subset selection Machine learning Jaccard coefficient Rough set Adjacency matrix Indiscernibility relation 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Bhawna Tibrewal
    • 1
  • Gargi Sur Chaudhury
    • 1
  • Sanjay Chakraborty
    • 2
  • Animesh Kairi
    • 3
  1. 1.Computer Science and Engineering DepartmentInstitute of Engineering and ManagementKolkataIndia
  2. 2.Department of Information TechnologyTechnoIndiaSalt Lake, KolkataIndia
  3. 3.Department of Information TechnologyInstitute of Engineering and ManagementSalt Lake, KolkataIndia

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