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Interval Chi-Square Score (ICSS): Feature Selection of Interval Valued Data

  • D. S. Guru
  • N. Vinay KumarEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)

Abstract

In this paper, a novel feature ranking criterion suitable for interval valued feature selection is proposed. The proposed criterion simulates the characteristics of the well known statistical criterion - chi-square in selecting the interval valued features effectively and hence called as Interval Chi-Square Score. Moreover, the paper also highlights the alternative approach proposed for computing the frequency of the distribution of interval valued data. For experimentation purpose, two standard benchmarking interval valued datasets are used with a suitable symbolic classifier for classification. The performance of the proposed ranking criterion is evaluated in terms of accuracy and the results are comparatively better than the contemporary interval valued feature selection methods.

Keywords

Interval valued data Symbolic feature selection Chi-square score 

Notes

Acknowledgement

The author N Vinay Kumar acknowledges the Department of Science & Technology, Govt. of India for their financial support rendered in terms of DST-INSPIRE fellowship.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Studies in Computer ScienceUniversity of MysoreMysuruIndia

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