Mahalanobis-Taguchi System (MTS), as a pattern recognition method by constructing a continuous measurement scale, has a very good performance on classification and feature selection for real-valued data. However, the record of symbolic interval data has become a common practice with the recent advances in database technologies. Kernel methods not only are powerful statistical nonlinear learning methods, but also can be defined over objects as diverse as graphs, sets, strings, and text documents. In this paper, we derive kernel Mahalanobis distance (KMD) to extend MTS to symbolic interval data. To evaluate the proposed method, four experiments with synthetic symbolic interval data sets and seven experiments with real symbolic interval data sets are performed and we have compared our method with MTS based on interval Mahalanobis distance (IMD). The experimental results show our method has a better classification performance than MTS based on IMD on Accuracy, Specificity, Sensitivity, and G-means. However, MTS based on IMD has a stronger dimension reduction rate than our method.
This paper recognizes multidimensional poverty in rural China using the Mahalanobis-Taguchi System on the China Labor-force Dynamic survey (CLDS) 2014 dataset. Six dimensions are included: Income, Education, health, living, asset and housing. Results suggest that the MTS can precisely recognize poor and non-poor households and select the main indexes leading to multidimensional poverty. Enhancing income and improving farming efficiency are the main means of poverty reduction. Moreover, improving rural living and health conditions by strengthening public services and infrastructure should also be a policymaking concern.
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