Please use this identifier to cite or link to this item: http://infotec.repositorioinstitucional.mx/jspui/handle/1027/392
A Family of Classifiers based on Feature Space Transformations and Model Selection
José Ortiz Bejar
MARIO GRAFF GUERRERO
Eric Sadit Téllez Avila
Acceso Abierto
Atribución-NoComercial-CompartirIgual
Ciencia de datos
Tecnologías de la información y comunicación
Datos estadísticos
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
INFOTEC Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación
2020-04
Trabajo de grado, doctorado
Español
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.
OTRAS
Versión publicada
publishedVersion - Versión publicada
Appears in Collections:Doctorado en Ciencias en Ciencia de Datos

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