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http://hdl.handle.net/10609/89668
Title: | Reducción de la dimensionalidad mediante métodos de selección de características en microarrays de ADN |
Author: | Maseda Tarin, Miguel |
Tutor: | Isern, David |
Others: | Ventura, Carles |
Abstract: | This document deals with the curse of dimensionality that we can find in datasets, to be specific, in those datasets where the number of features are counted as hundreds or thousands in each sample. We are looking for an improvement in the classification of our dataset through feature selection methods. The application of feature selection methods to datasets aims to reduce their dimensionality, with the intention of finding a feature subset that explains the problem in the appropriate way. With that in mind, we will use one benchmark dataset in the DNA microarrays studies, we will apply four feature selection methods so that we can verify the results with three different machine learning algorithms. We will use two filter methods (f-score and mRMR), one wrapper (SFS_forward) and, specially, a hybrid method, an adaptation of the work . We can observe the different advantages and issues offered by each of the feature selection methods and the different results that we obtain based on the machine learning method used for the classification task. |
Keywords: | dimensionality reduction hybrid methods feature selection |
Document type: | info:eu-repo/semantics/bachelorThesis |
Issue Date: | 2-Jan-2019 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Appears in Collections: | Bachelor thesis, research projects, etc. |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
mmasedaTFG0119_Video_Presentación.mp4 | 161,78 MB | MP4 | View/Open | |
mmasedaTFG0119_Presentación.pptx | 2,57 MB | Microsoft Powerpoint XML | View/Open | |
TFG_basic_functions.ipynb | 31,72 kB | Unknown | View/Open | |
TFG_F_W_Methods.ipynb | 1,86 MB | Unknown | View/Open | |
TFG_Hybrid_Method.ipynb | 3,03 MB | Unknown | View/Open | |
mmasedaTFG0119memoria.pdf | Memoria del TFG | 2,91 MB | Adobe PDF | View/Open |
mmasedaTFG0119presentación.pdf | Presentación en PDF del TFG | 2,5 MB | Adobe PDF | View/Open |
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