A nominal association matrix with feature selection for categorical data

作者：Huang, Wenxue^*; Shi, Yong; Wang, Xiaogang

来源：Communications in Statistics - Theory and Methods, 2017, 46(16): 7798-7819.

DOI：10.1080/03610926.2014.930911

• Association matrixl
• Association vector
• Categorical data
• Feature selection
• Proportional prediction
• The GK-tau

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.

• 出版日期2017
• 单位广州大学; 中国科学院大学