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Weighted distance-based trees for ranking data

Author

Listed:
  • Antonella Plaia

    (University of Palermo)

  • Mariangela Sciandra

    (University of Palermo)

Abstract

Within the framework of preference rankings, the interest can lie in finding which predictors and which interactions are able to explain the observed preference structures, because preference decisions will usually depend on the characteristics of both the judges and the objects being judged. This work proposes the use of a univariate decision tree for ranking data based on the weighted distances for complete and incomplete rankings, and considers the area under the ROC curve both for pruning and model assessment. Two real and well-known datasets, the SUSHI preference data and the University ranking data, are used to display the performance of the methodology.

Suggested Citation

  • Antonella Plaia & Mariangela Sciandra, 2019. "Weighted distance-based trees for ranking data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(2), pages 427-444, June.
    • Handle: RePEc:spr:advdac:v:13:y:2019:i:2:d:10.1007_s11634-017-0306-x
    DOI: 10.1007/s11634-017-0306-x
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    References listed on IDEAS

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    1. Lee, Paul H. & Yu, Philip L.H., 2010. "Distance-based tree models for ranking data," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1672-1682, June.
    2. Cook, Wade D., 2006. "Distance-based and ad hoc consensus models in ordinal preference ranking," European Journal of Operational Research, Elsevier, vol. 172(2), pages 369-385, July.
    3. Shih, Yu-Shan, 2001. "Selecting the best splits for classification trees with categorical variables," Statistics & Probability Letters, Elsevier, vol. 54(4), pages 341-345, October.
    4. Piccarreta, Raffaella, 2010. "Binary trees for dissimilarity data," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1516-1524, June.
    5. Amodio, S. & D’Ambrosio, A. & Siciliano, R., 2016. "Accurate algorithms for identifying the median ranking when dealing with weak and partial rankings under the Kemeny axiomatic approach," European Journal of Operational Research, Elsevier, vol. 249(2), pages 667-676.
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    Cited by:

    1. Antonella Plaia & Simona Buscemi & Mariangela Sciandra, 2021. "Consensus among preference rankings: a new weighted correlation coefficient for linear and weak orderings," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(4), pages 1015-1037, December.
    2. Antonella Plaia & Simona Buscemi & Johannes Fürnkranz & Eneldo Loza Mencía, 2022. "Comparing Boosting and Bagging for Decision Trees of Rankings," Journal of Classification, Springer;The Classification Society, vol. 39(1), pages 78-99, March.
    3. Juan Moreno-Garcia & Benito Yáñez-Araque & Felipe Hernández-Perlines & Luis Rodriguez-Benitez, 2022. "An Aggregation Metric Based on Partitioning and Consensus for Asymmetric Distributions in Likert Scale Responses," Mathematics, MDPI, vol. 10(21), pages 1-17, November.

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