Influence of imbalanced datasets in the induction of Full Bayesian Classifiers
This project consists in three main tasks: first, an analysis of the current state of the art in technologies for dealing with class imbalance problems in machine learning algorithms. Second, the analysis of how this problem actually affects a particular class of statistical models, the Bayesian Cla...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universitat Oberta de Catalunya (UOC) |
| Repositorio: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/83566 |
| Acceso en línea: | http://hdl.handle.net/10609/83566 |
| Access Level: | acceso abierto |
| Palabra clave: | Bayesian networks machine learning imbalance aprendizaje automático desequilibrio redes bayesianas aprenentatge automàtic desequilibri xarxes bayesianes Computer algorithms -- TFM Algorismes computacionals -- TFM Algoritmos computacionales -- TFM |
| Sumario: | This project consists in three main tasks: first, an analysis of the current state of the art in technologies for dealing with class imbalance problems in machine learning algorithms. Second, the analysis of how this problem actually affects a particular class of statistical models, the Bayesian Classifiers, proposing solutions to the particular problems found. And third, to implement a Bayesian Classifier and develop a series of experiments that would support the assertions of the analysis, and shed more light on how this problem can be dealt with. |
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