The evidence-theoretic k-NN rule for rank-ordered data: Application to predict an individual’s source of loan
© Springer International Publishing Switzerland 2014. We adapted the nonparametric evidence-theoretic k-Nearest Neighbor (k-NN) rule,whichwas originally designed formultinomial choice data, to rank-ordered choice data.The contribution of thismodel is its ability to extract information from all the o...
محفوظ في:
| المؤلفون الرئيسيون: | , , , |
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| التنسيق: | Book Series |
| منشور في: |
2018
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| الوصول للمادة أونلاين: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84921510354&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/45670 |
| الوسوم: |
إضافة وسم
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| المؤسسة: | Chiang Mai University |
| الملخص: | © Springer International Publishing Switzerland 2014. We adapted the nonparametric evidence-theoretic k-Nearest Neighbor (k-NN) rule,whichwas originally designed formultinomial choice data, to rank-ordered choice data.The contribution of thismodel is its ability to extract information from all the observed rankings to improve the prediction power for each individual’s primary choice. The evidence-theoretic k-NNrule for heterogeneous rank-ordered datamethod can be consistently applied to complete and partial rank-ordered choice data. This model was used to predict an individual’s source of loan given his or her characteristics and also identify individual characteristics that help the prediction. The results show that the prediction from the rank-ordered choice model outperforms that of the traditionalmultinomial choicemodelwith only one observed choice. |
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