6533b7dbfe1ef96bd12715cb
RESEARCH PRODUCT
Weighted distance-based trees for ranking data
Mariangela SciandraAntonella Plaiasubject
Statistics and ProbabilityDecision tree03 medical and health sciences0302 clinical medicine0504 sociology030225 pediatricsPreference dataStatisticsDecision treePruning (decision trees)University ranking dataDistance-based methodMathematicsWeighted distanceApplied Mathematics05 social sciencesUnivariate050401 social sciences methodsSUSHI dataComputer Science Applications1707 Computer Vision and Pattern RecognitionPreferenceComputer Science ApplicationsRankingRanking dataKemeny distanceSettore SECS-S/01 - StatisticaArea under the roc curvedescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-12-16 |