Comparative of resampling methods for predictive modeling in social networks
Realitzat a/ambIllinois Institute of Technology
Tipus de documentProjecte/Treball Final de Carrera
Data2013-12-18
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
[ANGLÈS] The aim of this project is to give some insight within the issue of applying resampling methods over correlated sets of data for predictive modeling, specifically social networks. These resampling methods were constructed over the principle of independence between samples, a principle that is virtually never satisfied in relational data. This project constructs a probabilistic network model, referred to as ground truth, and observes the behavior and performance of a simple prediction rule in conjunction with cross-validation and bootstrapping resampling methods. This project also enters in the issue of maintaining, or not, the correlation in the attribute values of the nodes present on the original data when a specific resample, whether it is for train or test, is withdrawn. We call the process of eliminating this correlation as reconstruction; which is essentially rebuilding the network with the extracted resample and re-computing the nodes’ attributes, erasing the influence of the nodes that are not present in the set. The results show a thorough comparison of the different resampling methodologies and also a strong compromise in the estimations whether reconstruction is present or not.
Descripció
Projecte realitzat en el marc d’un programa de mobilitat amb L'Illinois Institute of Technology in Chicago
TitulacióENGINYERIA DE TELECOMUNICACIÓ (Pla 1992)
Fitxers | Descripció | Mida | Format | Visualitza |
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PFC - Compariso ... ing in social networks.pdf | 1,087Mb | Visualitza/Obre |