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...
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th-cmuir.6653943832-456702018-01-24T06:14:51Z The evidence-theoretic k-NN rule for rank-ordered data: Application to predict an individual’s source of loan Supanika Leurcharusmee Peerapat Jatukannyaprateep Songsak Sriboonchitta Thierry Denoeux © 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. 2018-01-24T06:14:51Z 2018-01-24T06:14:51Z 2014-01-01 Book Series 16113349 03029743 2-s2.0-84921510354 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84921510354&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/45670 |
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© 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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Book Series |
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Supanika Leurcharusmee Peerapat Jatukannyaprateep Songsak Sriboonchitta Thierry Denoeux |
spellingShingle |
Supanika Leurcharusmee Peerapat Jatukannyaprateep Songsak Sriboonchitta Thierry Denoeux The evidence-theoretic k-NN rule for rank-ordered data: Application to predict an individual’s source of loan |
author_facet |
Supanika Leurcharusmee Peerapat Jatukannyaprateep Songsak Sriboonchitta Thierry Denoeux |
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Supanika Leurcharusmee |
title |
The evidence-theoretic k-NN rule for rank-ordered data: Application to predict an individual’s source of loan |
title_short |
The evidence-theoretic k-NN rule for rank-ordered data: Application to predict an individual’s source of loan |
title_full |
The evidence-theoretic k-NN rule for rank-ordered data: Application to predict an individual’s source of loan |
title_fullStr |
The evidence-theoretic k-NN rule for rank-ordered data: Application to predict an individual’s source of loan |
title_full_unstemmed |
The evidence-theoretic k-NN rule for rank-ordered data: Application to predict an individual’s source of loan |
title_sort |
evidence-theoretic k-nn rule for rank-ordered data: application to predict an individual’s source of loan |
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2018 |
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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84921510354&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/45670 |
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1681422788668162048 |