Empirical Study on Indicators Selection Model Based on Nonparametric K-Nearest Neighbor Identification and R Clustering Analysis
The combination of the nonparametric K-nearest neighbor discriminant method and R cluster analysis is used to construct a double-combination index screening model. The characteristics of the article are as follows: firstly, the nonparametric K-nearest neighbor discriminant method is used to select t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/2067065 |
| Summary: | The combination of the nonparametric K-nearest neighbor discriminant method and R cluster analysis is used to construct a double-combination index screening model. The characteristics of the article are as follows: firstly, the nonparametric K-nearest neighbor discriminant method is used to select the indicators which have significant ability to discriminate the default loss rate, which makes up the shortcomings of the previous research that only focuses on the indicators with significant ability to discriminate default state. Additionally, the R cluster analysis applied in this paper sorts the indicators by criterion class, rather than sorting the indicator by the whole index system. This approach ensures that indicators which are clustered in one class have the same economic implications and data characteristics. This approach avoids the situation where indicators that are clustered in one class only have the same data characteristics but have different economic implications. |
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| ISSN: | 1076-2787 1099-0526 |