Método de selección de atributos por clase
In many domains it is required to solve classification problems where the objects of study are described with a large number of features, many of which can be redundant and/or irrelevant. In order to improve the quality in classification it is necessary to eliminate this kind of features. Feature se...
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| Format: | master thesis |
| Status: | Versión aceptada para publicación |
| Publication Date: | 2009 |
| Country: | México |
| Institution: | Instituto Nacional de Astrofísica, Óptica y Electrónica |
| Repository: | Repositorio Institucional del INAOE |
| Language: | Spanish |
| OAI Identifier: | oai:inaoe.repositorioinstitucional.mx:1009/432 |
| Online Access: | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/432 |
| Access Level: | Open access |
| Keyword: | info:eu-repo/classification/Aprendizaje supervisado/Supervised learning info:eu-repo/classification/Reconocimiento de patrones/Pattern recognition info:eu-repo/classification/Selección de características/Feature selection info:eu-repo/classification/Clasificación de patrones./Pattern classification info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 |
| Summary: | In many domains it is required to solve classification problems where the objects of study are described with a large number of features, many of which can be redundant and/or irrelevant. In order to improve the quality in classification it is necessary to eliminate this kind of features. Feature selection has been widely used for the elimination of redundant and/or irrelevant features. There are two types for feature selection: 1. Feature selection for all classes. 2. Feature selection by class. Feature selection by class emerges with the idea that each class of a classification problem may have different properties and it should be described by a different feature subset. In this thesis a method of feature selection by class. The proposed method allows, by applying the one-against-all class binarization technique, the use of conventional feature selectors. Because supervised classifiers do not allow using a different feature subset for each class, it is also proposed to use a classifier ensemble and a new strategy decision for taking advantage of feature selection by class. The experimental results showed that in most cases the classification accuracy is improved when feature selection by class is used, compared against feature selection for all classes or without feature selection. |
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