Generalizações da integral de Choquet como método de combinação em comitês de classificadores
Ensembles of classifiers is an method in machine learning that consists in a collection of classifiers that process the same information and their output is combined in some manner. The process of classification is done in two main steps: the classification step and the combination step. In the cl...
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| Formato: | doctoralThesis |
| Idioma: | pt_BR |
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Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/48233 |
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| Resumo: | Ensembles of classifiers is an method in machine learning that consists in a collection of
classifiers that process the same information and their output is combined in some manner. The process of classification is done in two main steps: the classification step and the
combination step. In the classification step, each classifier processes the information and
provides an output, in the combination step, the output of every classifier is combined,
providing a single output. Although the combination step is extremely important, most
works focus mostly on the classification step. Therefore, in this work, generalizations of
the Choquet Integral will be proposed to be used as a combination method in ensembles of classifiers. The main idea is to allow a greater freedom of choice for functions
in the integral, opening possibilities for otimization and using functions adequate to the
data. Furthermore, a new notion of partial monotonicity is proposed, and consequently
an alternative to the notion of pre-aggregation functions. Preliminary results that were
obtained by the generalizations of the Choquet integral in the ensemble showed that they
were capable of obtaining good results, having a superior performance to known methods
in literature such as XGBoost, Bagging, among others. Furthermore, the generalizations
that used the proposed aggregation functions had good performance when compared to
other classes of functions, such as Copulas and Overlaps. |
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