Abstract
We adapted the nonparametric evidence-theoretic k-Nearest Neighbor (k-NN) rule, which was originally designed for multinomial choice data, to rank-ordered choice data. The contribution of this model 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-NN rule for heterogeneous rank-ordered data method 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 traditional multinomial choice model with only one observed choice.
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References
Alpaydin, E.: Introduction to Machine Learning. MIT Press (2004)
Baltas, G.: A Model for Multiple Brand Choice. European Journal of Operational Research 154(1), 144–149 (2004)
Beggs, S., Cardell, S., Hausman, J.: Assessing the Potential Demand for Electric Cars. Journal of Econometrics 17(1), 1–19 (1981)
Bhat, C.R., Srinivasan, S.: A Multidimensional Mixed Ordered-response Model for Analyzing Weekend Activity Participation. Transportation Research Part B: Methodological 39(3), 255–278 (2005)
Bhat, C.R., Srinivasan, S., Sen, S.: A Joint Model for the Perfect and Imperfect Substitute Goods Case: Application to Activity Time-use Decisions. Transportation Research Part B: Methodological 40(10), 827–850 (2006)
Cobb, B.R., Shenoy, P.P.: On the Plausibility Transformation Method for Translating Belief Function Models to Probability Models. International Journal of Approximate Reasoning 41(3), 314–330 (2006)
Denoeux, T.: A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Transactions on Systems, Man and Cybernetics 25(5), 804–813 (1995)
Denoeux, T.: Analysis of Evidence-Theoretic Decision Rules for Pattern Classification. Pattern recognition 30(7), 1095–1107 (1997)
Denoeux, T., Zouhal, L.M.: Handling Possibilistic Labels in Pattern Classification Using Evidential Reasoning. Fuzzy Sets and Systems 122(3), 409–424 (2001)
Maddala, G.S.: Limited-dependent and Qualitative Variables in Econometrics. Cambridge University Press (1986)
Mosteller, F., Tukey, J.W.: Data Analysis, Including Statistics. The Collected Works of John W. Tukey: Graphics 123(5), 1965–1985 (1988)
The Mathworks, Optimization Toolbox: User’s Guide (R2014b). The MathWorks, Inc. (2014)
Train, K.: Data analysis, Including Statistics Discrete Choice Methods with Simulation. Cambridge University Press (2009)
Zouhal, L.M., Denoeux, T.: An Evidence-theoretic k-NN Rule with Parameter Optimization. Systems, Man, and Cybernetics, Part C: Applications and Reviews, 28(2), 263-271 (1998)
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Leurcharusmee, S., Jatukannyaprateep, P., Sriboonchitta, S., Denoeux, T. (2014). The Evidence-Theoretic k-NN Rule for Rank-Ordered Data: Application to Predict an Individual’s Source of Loan. In: Cuzzolin, F. (eds) Belief Functions: Theory and Applications. BELIEF 2014. Lecture Notes in Computer Science(), vol 8764. Springer, Cham. https://doi.org/10.1007/978-3-319-11191-9_7
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DOI: https://doi.org/10.1007/978-3-319-11191-9_7
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11190-2
Online ISBN: 978-3-319-11191-9
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