Hierarchical classification with bayesian networks and chained classifiers
Hierarchical classification (HC) is a especial type of multi-label classification, that is, there is a set of labels, and instances can be associated to a subset of the labels. Nevertheless, in HC, the labels are arranged in a predefined structure, which is usually tree but in its general form is a...
| Autor: | |
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2019 |
| País: | México |
| Institución: | Instituto Nacional de Astrofísica, Óptica y Electrónica |
| Repositorio: | Repositorio Institucional del INAOE |
| Idioma: | inglés |
| OAI Identifier: | oai:inaoe.repositorioinstitucional.mx:1009/1948 |
| Acceso en línea: | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1948 |
| Access Level: | acceso abierto |
| Palabra clave: | info:eu-repo/classification/Inspec/Hierarchical classification info:eu-repo/classification/Inspec/Bayesian networks info:eu-repo/classification/Inspec/Artificial datasets info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 info:eu-repo/classification/cti/120323 |
| Sumario: | Hierarchical classification (HC) is a especial type of multi-label classification, that is, there is a set of labels, and instances can be associated to a subset of the labels. Nevertheless, in HC, the labels are arranged in a predefined structure, which is usually tree but in its general form is a Directed Acyclic Graph (DAG). Furthermore, in HC there are different problems, which can be described by the type of hierarchical structure, the number of paths which an instance can be associated and the depth of the paths. In this work is proposed a method for hierarchical classification, which can handle tree and DAG hierarchies, and predicts a single path which always reaches a leaf node. The method takes advantage of the hierarchical structure to inuence the prediction of local classifiers with their neighbors, to achieve this, two different strategies are combined. The first is to represent the hierarchical structure as a Bayesian network, which represents the data distribution in the nodes while maintains the hierarchical constraint; the second is to train chained classifiers, that feed the Bayesian network, in this way, the classifiers are considering the hierarchical structure. Furthermore, four different variants of the method were implemented, the main difference between them is the neighbors that inuence the predictions on the chained classifiers. Due to the different hierarchical classification problems, the real world datasets for each problem are limited. So, a way to evaluate or extend the analysis of a method is to generate Artificial Datasets (AD). Thus, a method to build artificial datasets for different hierarchical classification problems is proposed. The method generates instances from the distribution of each node, so, it requires as input the distribution for each leaf node, the distributions for internal nodes are estimated by the method. In this way, several artificial datasets have been generated, which are divided in two main groups, those with hierarchy tree type and those with hierarchy DAG type. Both groups were made available to the scientific community. Finally, the different variants of the proposed method for HC were evaluated with real world and artificial datasets. Later, their results were compared against standard and state of the art methods, then all the results were analyzed with Friedman test and its post-hoc the Nemenyi test. |
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