@PhDThesis{Parreira:2017:MéClHi,
               author = "Parreira, Michelle de Oliveira",
                title = "HSMI: m{\'e}todo de classifica{\c{c}}{\~a}o hier{\'a}rquico 
                         baseado em SVM multikernel com otimiza{\c{c}}{\~a}o 
                         meta-heur{\'{\i}}stica",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2017",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2016-11-24",
             keywords = "m{\'a}quinas de vetores de suporte, combina{\c{c}}{\~a}o de 
                         classificadores, classifica{\c{c}}{\~a}o bin{\'a}ria, 
                         sensoriamento remoto, reconhecimento de padr{\~o}es, support 
                         vector machine, ensembles, binary classification, remote sensing, 
                         pattern recognition.",
             abstract = "Esse trabalho prop{\~o}e o m{\'e}todo HSMI (Hierarchical Support 
                         vector machine with Multiple kernels optimized by Invasive weed 
                         optimization) de classifica{\c{c}}{\~a}o baseado em 
                         m{\'a}quinas de vetores suporte (SVM) que usa m{\'u}ltiplos 
                         kernels e atribui os r{\'o}tulos {\`a}s classes de modo 
                         hier{\'a}rquico. Uma {\'a}rvore bin{\'a}ria {\'e} criada 
                         automaticamente pelo algoritmo proposto e cada n{\'o} realiza a 
                         classifica{\c{c}}{\~a}o entre duas parti{\c{c}}{\~o}es do 
                         conjunto de classes pr{\'e}-classificado pelo n{\'o} superior. A 
                         classifica{\c{c}}{\~a}o {\'e} realizada pelo classificador SVM 
                         com m{\'u}ltiplos kernels combinados aproveitando as diferentes 
                         caracter{\'{\i}}sticas de cada kernel. A escolha pelas classes 
                         que comp{\~o}em cada parti{\c{c}}{\~a}o em cada n{\'o} {\'e} 
                         feita por otimiza{\c{c}}{\~a}o junto com os par{\^a}metros dos 
                         kernels e os coeficientes da combina{\c{c}}{\~a}o linear entre 
                         eles. Para isso {\'e} empregado o algoritmo 
                         Infesta{\c{c}}{\~a}o por Ervas Daninhas (Invasive Weed 
                         Optimization, IWO). Esse novo m{\'e}todo consegue separar 
                         hierarquicamente as classes com melhor separabilidade segundo um 
                         classificador SVM multikernel otimizado para cada 
                         classifica{\c{c}}{\~a}o bin{\'a}ria. Os resultados foram 
                         comparados com o m{\'e}todo SVM com kernel gaussiano e SVM com 
                         kernel polinomial. Os resultados demonstraram que o m{\'e}todo 
                         HSMI ao particionar as classes de forma embutida permite a 
                         fus{\~a}o de classes confusas identificadas no processo de 
                         classifica{\c{c}}{\~a}o. ABSTRACT: This work proposes the 
                         classifier method HSMI (Hierarchical support vector machine with 
                         multiple kernels optimized by Invasive weed optimization) based on 
                         support vector machine (SVM) that uses multiple kernels and 
                         assigns the labels to classes in a hierarchical way. A binary tree 
                         is automatically created by the proposed algorithm and each node 
                         performs the classification between two partitions of the set of 
                         pre-sorted classes by the upper node. The classification is 
                         performed by the SVM classifier with multiple kernels combined 
                         taking advantage of the different characteristics of each kernel. 
                         The choice of the classes that make up each partition at each node 
                         is done by optimization along with the parameters of the kernels 
                         and the coefficients of the linear combination between them. For 
                         this the Invasive Weed Optimization algorithm (IWO) is used. This 
                         new method can separate hierarchically classes with better 
                         separability according to a multi-kernel SVM classifier optimized 
                         for each binary classification. The results were compared with the 
                         SVM method with Gaussian kernel and SVM with polynomial kernel. 
                         The results showed that the HSMI method in partitioning the 
                         classes of embedded form allows the fusion of confused classes 
                         identified in the classification process.",
            committee = "Santos, Rafael Duarte Coelho dos (presidente) and Dutra, Luciano 
                         Vieira (orientador) and Pantale{\~a}o, Eliana (orientadora) and 
                         Forster, Carlos Henrique Quartucci and Negri, Rog{\'e}rio Galante 
                         and Mascarenhas, Nelson Delfino d'{\'A}vila",
           copyholder = "SID/SCD",
         englishtitle = "HSMI: hierarchical classification method based on multi-kernel SVM 
                         with meta-heuristic optimization",
             language = "pt",
                pages = "111",
                  ibi = "8JMKD3MGP3W34P/3N7M8QP",
                  url = "http://urlib.net/ibi/8JMKD3MGP3W34P/3N7M8QP",
           targetfile = "publicacao.pdf",
        urlaccessdate = "28 abr. 2024"
}