Título
A Family of Classifiers based on Feature Space Transformations and Model Selection
Autor
José Ortiz Bejar
Colaborador
MARIO GRAFF GUERRERO (Asesor de tesis)
Eric Sadit Téllez Avila (Asesor de tesis)
Nivel de Acceso
Acceso Abierto
Materias
Resumen o descripción
Improving the performance of classifiers is the realm of feature mapping, prototype selection, and kernel function transformations; these techniques aim for reducing the complexity, and also, improving the accuracy of models. In particular, the research’s objective is to combine them to transform data’s shape into another more convenient distribution; such that some simple algorithms, such as Naïve Bayes and k-Nearest Neighbors, can produce competitive classifiers. In this work, we introduce a family of classifiers based on feature mapping and kernel functions, orchestrated by simple a model selection scheme that achieves excel in performance. We provide an extensive experimental comparison of our methods with sixteen popular classifiers over different datasets supporting our claims. In addition to their competitive performance, our statistical tests also found that our methods are statistically different among them, and thus, an effective family of classifiers.
Tesis
Editor
INFOTEC Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación
Fecha de publicación
abril de 2020
Tipo de publicación
Otro
Trabajo de grado, doctorado
Versión de la publicación
Versión publicada
Recurso de información
Formato
application/pdf
Idioma
Español
Sugerencia de citación
José Ortiz Bejar, 2020. A family of Classifiers based on Feature Space Transformations and Model Selection. INFOTEC Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación, Aguascalientes, México.
Repositorio Orígen
Repositorio Institucional de INFOTEC
Descargas
2164