Search results for "Preference data"
showing 7 items of 7 documents
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.
Projection Clustering Unfolding: A New Algorithm for Clustering Individuals or Items in a Preference Matrix
2020
In the framework of preference rankings, the interest can lie in clustering individuals or items in order to reduce the complexity of the preference space for an easier interpretation of collected data. The last years have seen a remarkable flowering of works about the use of decision tree for clustering preference vectors. As a matter of fact, decision trees are useful and intuitive, but they are very unstable: small perturbations bring big changes. This is the reason why it could be necessary to use more stable procedures in order to clustering ranking data. In this work, a Projection Clustering Unfolding (PCU) algorithm for preference data will be proposed in order to extract useful info…
Item weighted Kemeny distance for preference data
2019
Preference data represent a particular type of ranking data where a group of people gives their preferences over a set of alternatives. The traditional metrics between rankings don’t take into account that the importance of elements can be not uniform. In this paper the item weighted Kemeny distance is introduced and its properties demonstrated.
A new position weight correlation coefficient for consensus ranking process without ties
2019
Preference data represent a particular type of ranking data where a group of people gives their preferences over a set of alternatives. The traditional metrics between rankings do not take into account the importance of swapping elements similar among them (element weights) or elements belonging to the top (or to the bottom) of an ordering (position weights). Following the structure of the τx proposed by Emond and Mason and the class of weighted Kemeny–Snell distances, a proper rank correlation coefficient is defined for measuring the correlation among weighted position rankings without ties. The one‐to‐one correspondence between the weighted distance and the rank correlation coefficient ho…
A Projection Pursuit Algorithm for Preference Data
2018
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 last years have seen a remarkable owering of works about the use of decision tree for clustering preference vectors. As a matter of fact, decision trees are useful and intuitive, but they are very unstable: small perturbations bring big changes. This is the reason why it could be necessary to use more stable procedures in order to clustering ranking data. In this work, following the idea of Bolton (2003), a Projection Pursuit (PP) clustering algorithm for preference data will be proposed in order to extract useful inform…
Vertical take-off and landing air transport to provide tourist mobility.
2012
Abstract This paper examines helicopter transfer services to reach attractive and not very accessible tourist areas, taking Sicily and its minor islands, in the South of Italy, as a case study. We investigate the viability of helicopter scheduled services for tourists moving from/to airports or doing one day tours to visit far away places. The mode choice of tourists is simulated using random utility models employing stated preference data. Heli-shuttle service is planned in terms of fleet size, frequency, fare and location pattern of heliports. The paper also analyses how a public subsidy reducing fares might change the set of feasible connections.
Classification trees for preference data: a distance-based approach
2014
In the framework of preference rankings, when the interest lies in explaining which predictors and which interactions among predictors are able to explain the observed preference structures, the possibility to derive consensus measures using a classi cation tree represents a novelty and an important tool given its easy interpretability. In this work we propose the use of a multivariate decision tree where a weighted Kemeny distance is used both to evaluate the distances between rankings and to de ne an impurity measure to be used in the recursive partitioning. The proposed approach allows also to weight di erently high distances in rankings in the top and in the bottom alternatives.