Search results for " Ranking data"
showing 5 items of 5 documents
A weighted distance-based approach with boosted decision trees for label ranking
2023
Label Ranking (LR) is an emerging non-standard supervised classification problem with practical applications in different research fields. The Label Ranking task aims at building preference models that learn to order a finite set of labels based on a set of predictor features. One of the most successful approaches to tackling the LR problem consists of using decision tree ensemble models, such as bagging, random forest, and boosting. However, these approaches, coming from the classical unweighted rank correlation measures, are not sensitive to label importance. Nevertheless, in many settings, failing to predict the ranking position of a highly relevant label should be considered more seriou…
Recursive partitioning: an approach based on the weighted kemeny distance
2015
In the framework of preference rankings, the interest can lie in finding which predictors and which interactions are able to explain the observed preference structures. The possibility to derive consensus measures using a classification tree represents a novelty and an important tool, given its easy interpretability. This work proposes the use of a univariate decision tree for ranking data based on the weighted Kemeny distance. The performance of the methodology will be shown by using a real dataset about university rankings.
New Flexible Probability Distributions for Ranking Data
2015
Recently, several models have been proposed in literature for analyzing ranks assigned by people to some object. These models summarize the liking feeling for this object, possibly also with respect to a set of explanatory variables. Some recent works have suggested the use of the Shifted Binomial and of the Inverse Hypergeometric distribution for modelling the approval rate, while mixture models have been developed for taking into account the uncertainty of the ranking process. We propose two new probabilistic models, based on the Discrete Beta and the Shifted-Beta Binomial distributions, that ensure much flexibility and allow the joint modelling of the scale (approval rate) and the shape …
Weighted distance-based trees for ranking data
2017
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.
Boosting for ranking data: an extension to item weighting
2021
Gli alberi decisionali sono una tecnica predittiva di machine learning particolarmente diffusa, utilizzata per prevedere delle variabili discrete (classificazione) o continue (regressione). Gli algoritmi alla base di queste tecniche sono intuitivi e interpretabili, ma anche instabili. Infatti, per rendere la classificazione più affidabile si `e soliti combinare l’output di più alberi. In letteratura, sono stati proposti diversi approcci per classificare ranking data attraverso gli alberi decisionali, ma nessuno di questi tiene conto ne dell’importanza, ne delle somiglianza dei singoli elementi di ogni ranking. L’obiettivo di questo articolo `e di proporre un’estensione ponderata del metodo …