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Computación y Sistemas

versión impresa ISSN 1405-5546

Comp. y Sist. vol.13 no.4 México abr./jun. 2010

 

Resumen de tesis doctoral

 

Prototype Selection Methods

 

Métodos para la selección de prototipos

 

Graduated: José Arturo Olvera López
National Institute of Astrophysics, Optics and Electronics Luis Enrique Erro # 1, Santa María Tonantzintla, C.P. 72840, Puebla, México. aolvera@inaoep.mx

Advisor: Jesús Ariel Carrasco Ochoa
National Institute of Astrophysics, Optics and Electronics Luis Enrique Erro # 1, Santa María Tonantzintla, C.P. 72840, Puebla, México. ariel@inaoep.mx

Advisor: José Francisco Martínez Trinidad
National Institute of Astrophysics, Optics and Electronics Luis Enrique Erro # 1, Santa María Tonantzintla, C.P. 72840, Puebla, México. fmartine@inaoep.mx

 

Graduated on April 16th, 2009

 

Abstract

In pattern recognition, supervised classifiers assign a class to unseen objects or prototypes. For classifying new prototypes a training set is used which provides information to the classifiers during the training stage. In practice, not all information in a training set is useful therefore it is possible to discard some irrelevant prototypes. This process is known as prototype selection and it is the main topic of this thesis. Through prototype selection the training set size is reduced which allows reducing the runtimes in the classification and/or training stages of classifiers.

Several methods have been proposed for selecting prototypes however their performance is strongly related to the use of a specific classifier and most of the methods spend long time for selecting prototypes when large datasets are processed.

In this thesis, four methods for selecting prototypes, which solve drawbacks of some methods in the state of the art are proposed. The first two methods are based on the sequential floating search and the two remaining methods are based on clustering and prototype relevance respectively.

Keywords: Prototype selection, Data Reduction, Sequential Selection, Border Prototypes.

 

Resumen

En reconocimiento de patrones, los clasificadores supervisados asignan una clase a nuevos objetos o prototipos. Para clasificar prototipos se usa un conjunto de entrenamiento el cual proporciona información a los clasificadores durante la etapa de entrenamiento. En la práctica, no toda la información en los conjuntos de entrenamiento es útil, por lo que se pueden descartar prototipos irrelevantes. A este proceso se le denomina selección de prototipos, el cual es el tema central de esta tesis.

Mediante la selección de prototipos se reduce el tamaño de los conjuntos de entrenamiento, lo cual permite una reducción en los tiempos de ejecución en las fases de clasificación o entrenamiento de los clasificadores. Se han propuesto diversos métodos para la selección de prototipos cuyo desempeño depende del uso de un clasificador particular, por otra parte, la mayoría de los métodos para la selección de prototipos son costosos, principalmente cuando se procesan grandes conjuntos de datos.

En esta tesis se presentan cuatro métodos para la selección de prototipos; dos de ellos se basan en la búsqueda secuencial flotante y los dos restantes en agrupamientos y relevancia de prototipos respectivamente.

Palabras clave: Selección de Prototipos, Reducción de Datos, Selección Secuencial, Prototipos Frontera.

 

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