Feature selection with iterative feature wighing methods
This work presents new algorithms for feature selection. The main propose is introduce the Relief algorithm to obtain an importance classification of the attributes to find which are less important. By removing the worst, an inducer will give us the performance to choose the best subset of data.
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| Format: | master thesis |
| Publication Date: | 2018 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | Spanish |
| OAI Identifier: | oai:upcommons.upc.edu:2117/117728 |
| Online Access: | https://hdl.handle.net/2117/117728 |
| Access Level: | Open access |
| Keyword: | Machine learning Algorithms relief selecció de variables feature selection Aprenentatge automàtic Algorismes Àrees temàtiques de la UPC::Informàtica |
| Summary: | This work presents new algorithms for feature selection. The main propose is introduce the Relief algorithm to obtain an importance classification of the attributes to find which are less important. By removing the worst, an inducer will give us the performance to choose the best subset of data. |
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