0000000000596871
AUTHOR
Sciandra Mariangela
showing 3 related works from this author
Weighted and unweighted distances based decision tree for ranking data
2018
Preference data represent a particular type of ranking data (widely used in sports, web search, social sciences), where a group of people gives their preferences over a set of alternatives. Within this framework, distance-based decision trees represent a non-parametric tool for identifying the profiles of subjects giving a similar ranking. This paper aims at detecting, in the framework of (complete and incomplete) ranking data, the impact of the differently structured weighted distances for building decision trees. The traditional metrics between rankings don’t take into account the importance of swapping elements similar among them (element weights) or elements belonging to the top (or to …
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…
DISTANCE‐BASED DECISION TREES FOR RANKING DATA: THE ROLE OF THE WEIGHT SYSTEMS
2018
In everyday life ranking and classification are basic cognitive skills that people use in order to grade everything that they experience. Grouping and ordering a set of elements is considered easy and communicative; thus, rankings of sport‐teams, universities, countries and so on are often observed. A particular case of ranking data is represented by preference data, where individuals show their preferences over a set of items. When individuals specific characteristics are available, an important issue concerns the identification of the profiles of respondents (or judges) giving the same/similar rankings. In order to incorporate respondent‐specific covariates distance‐based decision tree mo…