000224079 001__ 224079
000224079 005__ 20190317000612.0
000224079 0247_ $$2doi$$a10.1109/DSAA.2016.31
000224079 02470 $$2ISI$$a000391583800025
000224079 037__ $$aCONF
000224079 245__ $$aOnline Collaborative Prediction of Regional Vote Results
000224079 269__ $$a2016
000224079 260__ $$bIEEE$$c2016$$aNew York
000224079 300__ $$a10
000224079 336__ $$aConference Papers
000224079 520__ $$aWe consider online predictions of vote results, where regions across a country vote on an issue under discussion. Such online predictions before and during the day of the vote are useful to media agencies, polling institutes, and political parties, e.g., to identify regions that are crucial in determining the national outcome of a vote. We analyze a unique dataset from Switzerland. The dataset contains 281 votes from 2352 regions over a period of 34 years. We make several contributions towards improving online predictions. First, we show that these votes exhibit a bi-clustering of the vote results, i.e., regions that are spatially close tend to vote similarly, and issues that discuss similar topics show similar global voting patterns. Second, we develop models that can exploit this bi-clustering, as well as the features associated with the votes and regions. Third, we show that, when combining vote results and features together, Bayesian methods are essential to obtaining good performance. Our results show that Bayesian methods give better estimates of the hyperparameters than non-Bayesian methods such as cross-validation. The resulting models generalize well to many different tasks, produce robust predictions, and are easily interpretable.
000224079 6531_ $$abayesian methods
000224079 6531_ $$avote prediction
000224079 6531_ $$apolitical data mining
000224079 6531_ $$aregression
000224079 6531_ $$amatrix factorization
000224079 6531_ $$agaussian process
000224079 700__ $$0245633$$g161149$$aEtter, Vincent
000224079 700__ $$0246911$$g228491$$aKhan, Mohammad Emtiyaz
000224079 700__ $$0241029$$g152655$$aGrossglauser, Matthias
000224079 700__ $$0240373$$g103925$$aThiran, Patrick
000224079 7112_ $$d17-19 October 2016$$cMontreal, QC, Canada$$a2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)
000224079 773__ $$t2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)$$q233-242
000224079 8564_ $$uhttp://ieeexplore.ieee.org/document/7796909/$$zURL
000224079 8564_ $$uhttps://infoscience.epfl.ch/record/224079/files/dsaa2016_votepredict.pdf$$zPublisher's version$$s2553019$$yPublisher's version
000224079 909C0 $$xU10836$$0252455$$pLCA4
000224079 909C0 $$pLCA3$$xU10431$$0252454
000224079 909CO $$qGLOBAL_SET$$pconf$$pIC$$ooai:infoscience.tind.io:224079
000224079 917Z8 $$x152655
000224079 937__ $$aEPFL-CONF-224079
000224079 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000224079 980__ $$aCONF