Facebook pixel tracking

The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

Contact Info
Email info@thesai.org
Website thesai.org
Follow Us
Contact Info
Email info@thesai.org
Follow Us
Research Article | Open Access |

A Copula Statistic for Measuring Nonlinear Dependence with Application to Feature Selection in Machine Learning

Author 1: Mohsen Ben Hassine Author 2: Lamine Mili Author 3: Kiran Karra
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 8, No. 7 · Published 2017

DOI: https://doi.org/10.14569/IJACSA.2017.080720

Download PDF Cite Call for Papers

Abstract

Feature selection in machine learning aims to find out the best subset of variables from the input that reduces the computation requirement and improves the predictor performance. In this paper, a new index based on empirical copulas, termed the Copula Statistic (CoS) to assess the strength of statistical dependence and for testing statistical independence is introduced. It is shown that this test exhibits higher statistical power than other indices. Finally, the CoS is applied to feature selection in machine learning problems, which allow a demonstration of the good performance of the CoS.

Keywords

Copula multivariate dependence nonlinear systems feature selection machine learning

How to Cite this Article

Hassine, M. B., Mili, L., & Karra, K. (2017). A Copula Statistic for Measuring Nonlinear Dependence with Application to Feature Selection in Machine Learning. International Journal of Advanced Computer Science and Applications, 8(7). https://doi.org/10.14569/IJACSA.2017.080720

Hassine, Mohsen Ben, et al.. "A Copula Statistic for Measuring Nonlinear Dependence with Application to Feature Selection in Machine Learning." International Journal of Advanced Computer Science and Applications, vol. 8, no. 7, 2017, https://doi.org/10.14569/IJACSA.2017.080720.

@article{Hassine2017,
  title     = {A Copula Statistic for Measuring Nonlinear Dependence with Application to Feature Selection in Machine Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {7},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Mohsen Ben Hassine and Lamine Mili and Kiran Karra},
  doi       = {10.14569/IJACSA.2017.080720},
  url       = {https://doi.org/10.14569/IJACSA.2017.080720}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

Back to Issue
IJACSA Journal Cover

IJACSA