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Network Selection: A Method for Ranked Lists Selection
We consider the problem of finding the set of rankings that best represents a given group of orderings on the same collection of elements (preference lists). This problem arises from social choice and voting theory, in which each voter gives a preference on a set of alternatives, and a system output...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3427185/ https://www.ncbi.nlm.nih.gov/pubmed/22937075 http://dx.doi.org/10.1371/journal.pone.0043678 |
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author | Cutillo, Luisa Carissimo, Annamaria Figini, Silvia |
author_facet | Cutillo, Luisa Carissimo, Annamaria Figini, Silvia |
author_sort | Cutillo, Luisa |
collection | PubMed |
description | We consider the problem of finding the set of rankings that best represents a given group of orderings on the same collection of elements (preference lists). This problem arises from social choice and voting theory, in which each voter gives a preference on a set of alternatives, and a system outputs a single preference order based on the observed voters’ preferences. In this paper, we observe that, if the given set of preference lists is not homogeneous, a unique true underling ranking might not exist. Moreover only the lists that share the highest amount of information should be aggregated, and thus multiple rankings might provide a more feasible solution to the problem. In this light, we propose Network Selection, an algorithm that, given a heterogeneous group of rankings, first discovers the different communities of homogeneous rankings and then combines only the rank orderings belonging to the same community into a single final ordering. Our novel approach is inspired by graph theory; indeed our set of lists can be loosely read as the nodes of a network. As a consequence, only the lists populating the same community in the network would then be aggregated. In order to highlight the strength of our proposal, we show an application both on simulated and on two real datasets, namely a financial and a biological dataset. Experimental results on simulated data show that Network Selection can significantly outperform existing related methods. The other way around, the empirical evidence achieved on real financial data reveals that Network Selection is also able to select the most relevant variables in data mining predictive models, providing a clear superiority in terms of predictive power of the models built. Furthermore, we show the potentiality of our proposal in the bioinformatics field, providing an application to a biological microarray dataset. |
format | Online Article Text |
id | pubmed-3427185 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-34271852012-08-30 Network Selection: A Method for Ranked Lists Selection Cutillo, Luisa Carissimo, Annamaria Figini, Silvia PLoS One Research Article We consider the problem of finding the set of rankings that best represents a given group of orderings on the same collection of elements (preference lists). This problem arises from social choice and voting theory, in which each voter gives a preference on a set of alternatives, and a system outputs a single preference order based on the observed voters’ preferences. In this paper, we observe that, if the given set of preference lists is not homogeneous, a unique true underling ranking might not exist. Moreover only the lists that share the highest amount of information should be aggregated, and thus multiple rankings might provide a more feasible solution to the problem. In this light, we propose Network Selection, an algorithm that, given a heterogeneous group of rankings, first discovers the different communities of homogeneous rankings and then combines only the rank orderings belonging to the same community into a single final ordering. Our novel approach is inspired by graph theory; indeed our set of lists can be loosely read as the nodes of a network. As a consequence, only the lists populating the same community in the network would then be aggregated. In order to highlight the strength of our proposal, we show an application both on simulated and on two real datasets, namely a financial and a biological dataset. Experimental results on simulated data show that Network Selection can significantly outperform existing related methods. The other way around, the empirical evidence achieved on real financial data reveals that Network Selection is also able to select the most relevant variables in data mining predictive models, providing a clear superiority in terms of predictive power of the models built. Furthermore, we show the potentiality of our proposal in the bioinformatics field, providing an application to a biological microarray dataset. Public Library of Science 2012-08-24 /pmc/articles/PMC3427185/ /pubmed/22937075 http://dx.doi.org/10.1371/journal.pone.0043678 Text en © 2012 Cutillo et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Cutillo, Luisa Carissimo, Annamaria Figini, Silvia Network Selection: A Method for Ranked Lists Selection |
title | Network Selection: A Method for Ranked Lists Selection |
title_full | Network Selection: A Method for Ranked Lists Selection |
title_fullStr | Network Selection: A Method for Ranked Lists Selection |
title_full_unstemmed | Network Selection: A Method for Ranked Lists Selection |
title_short | Network Selection: A Method for Ranked Lists Selection |
title_sort | network selection: a method for ranked lists selection |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3427185/ https://www.ncbi.nlm.nih.gov/pubmed/22937075 http://dx.doi.org/10.1371/journal.pone.0043678 |
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