Desarrollo de clasificadores basados en reglas de asociación de clase
Classification based on Class Association Rules (CARs) or associative classification is a data mining technique that consists of, given a training instance set, finding certain characteristics in the instances in order to build rules that are subsequently used for classifying unseen instances. Assoc...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2011 |
| País: | México |
| Institución: | Instituto Nacional de Astrofísica, Óptica y Electrónica |
| Repositorio: | Repositorio Institucional del INAOE |
| Idioma: | español |
| OAI Identifier: | oai:inaoe.repositorioinstitucional.mx:1009/688 |
| Acceso en línea: | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/688 |
| Access Level: | acceso abierto |
| Palabra clave: | info:eu-repo/classification/Minería de datos/Data mining info:eu-repo/classification/Reconocimiento de patrones/Pattern recognition info:eu-repo/classification/Clasificación/Classification info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 |
| Sumario: | Classification based on Class Association Rules (CARs) or associative classification is a data mining technique that consists of, given a training instance set, finding certain characteristics in the instances in order to build rules that are subsequently used for classifying unseen instances. Associative classification has been used in different tasks, for example: text classification, text segmentation, and automatic image annotation, among others. However, associative classification methods still have some weaknesses. In this doctoral dissertation we propose an algorithm called CAR-CA, which introduces a new pruning strategy that allows to obtain specific rules with high values of the quality measure. Besides, we introduce two classifiers based on CARs, CAR-IC and CAR-NF, both use a new way for ordering the set of CARs based on the rule size, a new covering criterion that considers the inexact coverage when any rule covers the new instance, and a new strategy for deciding the class of a new instance. Additionally, these classifiers use as threshold for the quality measure, the minimum value that avoids ambiguity at the classification stage. In particular, The CAR-NF classifier introduces the use of the Netconf measure to compute the set of CARs. The experimental results show that the proposed CARs based classifiers CAR-IC and CAR-NF have better performance than the main successful classifiers based on CARs. |
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