Improved distance functions for the ReliefF family
This thesis continues from the preceding work from Robnik and Kononenko, where they demonstrate the importance and applicability of the Relief family of algorithms. This new dissertation emphasizes the augmentation of input data (numerical, categorical, and text data types) that the Relief algorithm...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/407783 |
| Acceso en línea: | https://hdl.handle.net/2117/407783 |
| Access Level: | acceso abierto |
| Palabra clave: | Algorithms Embeddings (Mathematics) Relief ReliefF RReliefF feature selection feature estimation quality estimates SBERT embeddings distance functions Algorismes Embeddings (Matemàtica) Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica::Algorísmica i teoria de la complexitat |
| Sumario: | This thesis continues from the preceding work from Robnik and Kononenko, where they demonstrate the importance and applicability of the Relief family of algorithms. This new dissertation emphasizes the augmentation of input data (numerical, categorical, and text data types) that the Relief algorithm can wield for two types of problems (classification and regression) while preserving relevant properties such as robustness and flexibility. Moreover, we provide an exhaustive in-depth analysis of the algorithm convergence. Stating how it works, and where are the major points of failure associated with applying the algorithm to real-world datasets. As well as valid arguments guided by well-known research experiments to reinforce our implementation choices. In addition to an empirical analysis with synthetic and real datasets. An available software framework is made public using open-source libraries. |
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