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A nominal association matrix with feature selection for categorical data

Author

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  • Wenxue Huang
  • Yong Shi
  • Xiaogang Wang

Abstract

An intrinsic association matrix is introduced to measure category-to-variable association based on proportional reduction of prediction error by an explanatory variable. The normalization of the diagonal gives rise to the expected rates of error-reduction and the off-diagonal yields expected distributions of the rates of error for all response categories. A general framework of association measures based on the proposed matrix is established using an application-specific weight vector. A hierarchy of equivalence relations defined by the association matrix and vector is shown. Applications to financial and survey data together with simulation results are presented.

Suggested Citation

  • Wenxue Huang & Yong Shi & Xiaogang Wang, 2017. "A nominal association matrix with feature selection for categorical data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(16), pages 7798-7819, August.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:16:p:7798-7819
    DOI: 10.1080/03610926.2014.930911
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