Classificação de dados combinando mapas auto-organizáveis com vizinho informativo mais próximo

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dc.contributor.advisorSilva, Leandro Augusto da
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/1396385111251741por
dc.contributor.authorMoreira, Lenadro Juvêncio
dc.creator.Latteshttp://lattes.cnpq.br/3927703815676178por
dc.date.accessioned2017-03-22T15:01:23Z
dc.date.accessioned2020-05-28T18:08:49Z
dc.date.available2020-05-28T18:08:49Z
dc.date.issued2016-12-14
dc.description.abstractThe data classification is a data mining task with relevant utilization in various areas of application, such as medicine, industry, marketing, financial market, teaching and many others. Although this task is an element search for many autors, there are open issues such as, e.g., in situations where there is so much data, noise data and unbalanced classes. In this way, this work will present a data classifier proposal that combines the SOM (Self-Organizing Map) neural network with INN (Informative Nearest Neighbors). The combination of these two algorithms will be called in this work as SOM-INN. Therefore, the SOM-INN process to classify a new object will be done in a first step with the SOM that has a functionality to map a reduced dataset through an approach that utilizes the prototype generation concept, also called the winning neuron and, in a second step, with the INN algorithm that is used to classify the new object through an approach that finds in the reduced dataset by SOM the most informative object. Were made experiments using 21 public datasets comparing classic data classification algorithms of the literature, from the indicators of reduction training set, accuracy, kappa and time consumed in the classification process. The results obtained show that the proposed SOM-INN algorithm, when compared with the others classifiers of the literature, presents better accuracy in databases where the border region is not well defined. The main differential of the SOM-INN is in the classification time, which is extremely important for real applications. Keywords: data classification; prototype generation; K nearest neighbors; self-organizingeng
dc.formatapplication/pdf*
dc.identifier.citationMOREIRA, Leandro Juvêncio. Classificação de dados combinando mapas auto-organizáveis com vizinho informativo mais próximo. 2016. 54 f. Dissertação ( Engenharia Elétrica) - Universidade Presbiteriana Mackenzie, São Paulo .por
dc.identifier.urihttp://dspace.mackenzie.br/handle/10899/24442
dc.keywordsdata classificationeng
dc.keywordsprototype generationeng
dc.keywordsk nearest neighbors (algorithm)eng
dc.keywordsself-organizing mapseng
dc.keywordsinformative nearest neighbors (algorithm)eng
dc.languageporpor
dc.publisherUniversidade Presbiteriana Mackenziepor
dc.rightsAcesso Abertopor
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectclassificação de dadospor
dc.subjectgeração de protótipospor
dc.subjectk vizinhos mais próximos (algoritmo)por
dc.subjectmapas auto-organizáveispor
dc.subjectvizinho informativo mais próximo (algoritmo)por
dc.subject.cnpqCNPQ::ENGENHARIAS::ENGENHARIA ELETRICA::TELECOMUNICACOESpor
dc.thumbnail.urlhttp://tede.mackenzie.br/jspui/retrieve/13441/LEANDRO%20JUVENCIO%20MOREIRA.pdf.jpg*
dc.titleClassificação de dados combinando mapas auto-organizáveis com vizinho informativo mais próximopor
dc.typeDissertaçãopor
local.contributor.board1Silva, Leandro Nunes de Castro
local.contributor.board1Latteshttp://lattes.cnpq.br/2741458816539568por
local.contributor.board2Pasti, Rodrigo
local.contributor.board2Latteshttp://lattes.cnpq.br/9305519410031191por
local.publisher.countryBrasilpor
local.publisher.departmentEscola de Engenharia Mackenzie (EE)por
local.publisher.initialsUPMpor
local.publisher.programEngenharia Elétricapor
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