Feature selection with iterative feature wighing methods

Rodoreda Valeri, Pol

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Rodoreda Valeri, Pol

Tutor / directorBelanche Muñoz, Luis Antonio

; Larrosa Bondia, Francisco Javier

Document typeMaster thesis

Date2018-01-17

Rights accessOpen Access

All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder

Abstract

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.

SubjectsMachine learning, Algorithms, Aprenentatge automàtic, Algorismes

DegreeMÀSTER UNIVERSITARI EN ENGINYERIA INFORMÀTICA (Pla 2012)

URIhttp://hdl.handle.net/2117/117728

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Màsters oficials - Màster universitari en Enginyeria Informàtica - MEI [105]

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Feature selection with iterative feature wighing methods

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