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Previous issue date: 2009-08-07 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / The objective of the researches in artificial intelligence is to qualify the computer to execute functions that are performed by humans using knowledge and reasoning. This work was developed in the area of machine learning, that it s the study branch of artificial intelligence, being related to the project and development of algorithms and techniques capable to allow the computational learning. The objective of this work is analyzing a feature selection method for ensemble systems. The proposed method is inserted into the filter approach of feature selection method, it s using the variance and Spearman correlation to rank the feature and using the reward and punishment strategies to measure the feature importance for the identification of the classes. For each ensemble, several different configuration were used, which varied from hybrid (homogeneous) to non-hybrid (heterogeneous) structures of ensemble. They were submitted to five combining methods (voting, sum, sum weight, multiLayer Perceptron and na?ve Bayes) which were applied in six distinct database (real and artificial). The classifiers applied during the experiments were k- nearest neighbor, multiLayer Perceptron, na?ve Bayes and decision tree. Finally, the performance of ensemble was analyzed
comparatively, using none feature selection method, using a filter approach (original) feature selection method and the proposed method. To do this comparison, a statistical test was applied, which demonstrate that there was a significant improvement in the precision of the ensembles / As pesquisas em intelig?ncia artificial t?m como objetivo capacitar o computador a executar fun??es que s?o desempenhadas pelo ser humano usando conhecimento e racioc?nio. O presente trabalho foi desenvolvido dentro da ?rea de aprendizado de m?quina (AM), que ? um ramo de estudo da intelig?ncia artificial, sendo relacionado ao projeto e desenvolvimento de algoritmos e t?cnicas capazes de permitir o aprendizado computacional. O objetivo deste
trabalho ? analisar um m?todo de sele??o de atributos em comit?s de classificadores. Esse m?todo, baseado em filtros, utilizou a vari?ncia e a correla??o de Spearman para ordenar os atributos e estrat?gias de recompensa e puni??o para medir a import?ncia de cada atributo na identifica??o das classes. Foram formados comit?s de classificadores tanto homog?neos quanto heterog?neos, e submetidos a cinco m?todos de combina??o de classificadores (voto, soma, soma ponderada, MLP e naive Bayes), os quais foram aplicados a seis bases de dados distintas (reais e artificiais). Os classificadores aplicados durante os experimentos foram k-nn,
MLP, naive Bayes e ?rvore de decis?o. Por fim, foram analisados, comparativamente, o esempenho dos comit?s de classificadores utilizando nenhum m?todo de sele??o de
atributos, utilizando um m?todo de sele??o de atributos padr?o baseado em filtro e o m?todo
proposto (RecPun). Com base em um teste estat?stico, foi demonstrado que houve uma melhora significante na precis?o dos comit?s
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.ufrn.br:123456789/17999 |
| Date | 07 August 2009 |
| Creators | Vale, Karliane Medeiros Ovidio |
| Contributors | CPF:66487099449, http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4790093J8, Carvalho, Bruno Motta de, CPF:79228860472, http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4791070J6, Cavalcanti, George Darmiton da Cunha, CPF:93568347415, http://lattes.cnpq.br/8577312109146354, Canuto, Anne Magaly de Paula |
| Publisher | Universidade Federal do Rio Grande do Norte, Programa de P?s-Gradua??o em Sistemas e Computa??o, UFRN, BR, Ci?ncia da Computa??o |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Format | application/pdf |
| Source | reponame:Repositório Institucional da UFRN, instname:Universidade Federal do Rio Grande do Norte, instacron:UFRN |
| Rights | info:eu-repo/semantics/openAccess |
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