Monte Carlo Evaluation of Classification Algorithms Based on Fisher's Linear Function in Classification of Patients With CHD
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFMG |
Texto Completo: | http://hdl.handle.net/1843/39709 |
Resumo: | Classification comprises a variety of problems, which are solved in several ways. The need for automatic classification methods arises in a number of areas, from voice recognition, to modern automobiles, to the recognition of tumors through x-rays to assist doctors, by classifying emails as legitimate or spam. Due to the importance and complexity of such problems, there is a need for methods that provide greater accuracy and interpretability of the results. Among them the Boosting methods, which have emerged in the field of computation, work by sequentially applying a classification algorithm to reweighted versions of the training data set, giving greater weight to erroneous observations. The aim of this study was to study the Fisher Linear Discriminant Analysis (LDA) model and the same one using Boosting algorithm (AdaBoost) in the presence / absence of coronary heart disease (CHD) problem in patients. The criteria used to make the comparisons were sensitivity, specificity, false positive rate and false negative rate. In addition, Monte Carlo simulation was performed to calculate these rates in different partitions of the training set. The Boosting method was successfully applied in LDA and provided a higher sensitivity than the conventional LDA. |
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2022-02-25T15:48:32Z2022-02-25T15:48:32Z201714104109e-22785728http://hdl.handle.net/1843/39709Classification comprises a variety of problems, which are solved in several ways. The need for automatic classification methods arises in a number of areas, from voice recognition, to modern automobiles, to the recognition of tumors through x-rays to assist doctors, by classifying emails as legitimate or spam. Due to the importance and complexity of such problems, there is a need for methods that provide greater accuracy and interpretability of the results. Among them the Boosting methods, which have emerged in the field of computation, work by sequentially applying a classification algorithm to reweighted versions of the training data set, giving greater weight to erroneous observations. The aim of this study was to study the Fisher Linear Discriminant Analysis (LDA) model and the same one using Boosting algorithm (AdaBoost) in the presence / absence of coronary heart disease (CHD) problem in patients. The criteria used to make the comparisons were sensitivity, specificity, false positive rate and false negative rate. In addition, Monte Carlo simulation was performed to calculate these rates in different partitions of the training set. The Boosting method was successfully applied in LDA and provided a higher sensitivity than the conventional LDA.A classificação compreende uma variedade de problemas, que são resolvidos de várias maneiras. A necessidade de métodos automáticos de classificação surge em diversas áreas, desde o reconhecimento de voz, até automóveis modernos, até o reconhecimento de tumores através de radiografias para auxiliar médicos, classificando e-mails como legítimos ou spam. Devido à importância e complexidade de tais problemas, há a necessidade de métodos que proporcionem maior precisão e interpretabilidade dos resultados. Dentre eles, os métodos Boosting, que surgiram no campo da computação, funcionam aplicando sequencialmente um algoritmo de classificação a versões reponderadas do conjunto de dados de treinamento, dando maior peso às observações errôneas. O objetivo deste estudo foi estudar o modelo de Análise Discriminante Linear de Fisher (LDA) e o mesmo utilizando o algoritmo Boosting (AdaBoost) na presença/ausência de problema de doença coronariana (DAC) em pacientes. Os critérios utilizados para fazer as comparações foram sensibilidade, especificidade, taxa de falsos positivos e taxa de falsos negativos. Além disso, a simulação de Monte Carlo foi realizada para calcular essas taxas em diferentes partições do conjunto de treinamento. O método Boosting foi aplicado com sucesso na LDA e proporcionou uma sensibilidade maior que a LDA convencional.engUniversidade Federal de Minas GeraisUFMGBrasilGABINETE - GABINETE - REITORIAIOSR Journal of MathematicsCoronary Heart DiseaseAdaBoostMachine LearningSensibilityMonte Carlo Evaluation of Classification Algorithms Based on Fisher's Linear Function in Classification of Patients With CHDAvaliação Monte Carlo de Algoritmos de Classificação Baseados na Função Linear de Fisher na Classificação de Pacientes com CCinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://www.iosrjournals.org/iosr-jm/pages/v13(1)Version-4.htmlGeraldo José Rodrigues LiskaGilberto Rodrigues LiskaGuido Gustavo Humada-gonzálezJuliano BortoliniCarlos José Dos Reisapplication/pdfinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGLICENSELicense.txtLicense.txttext/plain; charset=utf-82042https://repositorio.ufmg.br/bitstream/1843/39709/1/License.txtfa505098d172de0bc8864fc1287ffe22MD51ORIGINAL2017_Monte Carlo Evaluation of Classification Algorithms Based on.pdf2017_Monte Carlo Evaluation of Classification Algorithms Based on.pdfapplication/pdf243473https://repositorio.ufmg.br/bitstream/1843/39709/2/2017_Monte%20Carlo%20Evaluation%20of%20Classification%20Algorithms%20Based%20on.pdfd3c9ac47a041b2d1d3f31d7f0f432287MD521843/397092022-02-25 12:48:32.693oai:repositorio.ufmg.br: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Repositório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2022-02-25T15:48:32Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false |
dc.title.pt_BR.fl_str_mv |
Monte Carlo Evaluation of Classification Algorithms Based on Fisher's Linear Function in Classification of Patients With CHD |
dc.title.alternative.pt_BR.fl_str_mv |
Avaliação Monte Carlo de Algoritmos de Classificação Baseados na Função Linear de Fisher na Classificação de Pacientes com CC |
title |
Monte Carlo Evaluation of Classification Algorithms Based on Fisher's Linear Function in Classification of Patients With CHD |
spellingShingle |
Monte Carlo Evaluation of Classification Algorithms Based on Fisher's Linear Function in Classification of Patients With CHD Geraldo José Rodrigues Liska Coronary Heart Disease AdaBoost Machine Learning Sensibility |
title_short |
Monte Carlo Evaluation of Classification Algorithms Based on Fisher's Linear Function in Classification of Patients With CHD |
title_full |
Monte Carlo Evaluation of Classification Algorithms Based on Fisher's Linear Function in Classification of Patients With CHD |
title_fullStr |
Monte Carlo Evaluation of Classification Algorithms Based on Fisher's Linear Function in Classification of Patients With CHD |
title_full_unstemmed |
Monte Carlo Evaluation of Classification Algorithms Based on Fisher's Linear Function in Classification of Patients With CHD |
title_sort |
Monte Carlo Evaluation of Classification Algorithms Based on Fisher's Linear Function in Classification of Patients With CHD |
author |
Geraldo José Rodrigues Liska |
author_facet |
Geraldo José Rodrigues Liska Gilberto Rodrigues Liska Guido Gustavo Humada-gonzález Juliano Bortolini Carlos José Dos Reis |
author_role |
author |
author2 |
Gilberto Rodrigues Liska Guido Gustavo Humada-gonzález Juliano Bortolini Carlos José Dos Reis |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Geraldo José Rodrigues Liska Gilberto Rodrigues Liska Guido Gustavo Humada-gonzález Juliano Bortolini Carlos José Dos Reis |
dc.subject.other.pt_BR.fl_str_mv |
Coronary Heart Disease AdaBoost Machine Learning Sensibility |
topic |
Coronary Heart Disease AdaBoost Machine Learning Sensibility |
description |
Classification comprises a variety of problems, which are solved in several ways. The need for automatic classification methods arises in a number of areas, from voice recognition, to modern automobiles, to the recognition of tumors through x-rays to assist doctors, by classifying emails as legitimate or spam. Due to the importance and complexity of such problems, there is a need for methods that provide greater accuracy and interpretability of the results. Among them the Boosting methods, which have emerged in the field of computation, work by sequentially applying a classification algorithm to reweighted versions of the training data set, giving greater weight to erroneous observations. The aim of this study was to study the Fisher Linear Discriminant Analysis (LDA) model and the same one using Boosting algorithm (AdaBoost) in the presence / absence of coronary heart disease (CHD) problem in patients. The criteria used to make the comparisons were sensitivity, specificity, false positive rate and false negative rate. In addition, Monte Carlo simulation was performed to calculate these rates in different partitions of the training set. The Boosting method was successfully applied in LDA and provided a higher sensitivity than the conventional LDA. |
publishDate |
2017 |
dc.date.issued.fl_str_mv |
2017 |
dc.date.accessioned.fl_str_mv |
2022-02-25T15:48:32Z |
dc.date.available.fl_str_mv |
2022-02-25T15:48:32Z |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
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article |
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publishedVersion |
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http://hdl.handle.net/1843/39709 |
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e-22785728 |
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openAccess |
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application/pdf |
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Universidade Federal de Minas Gerais |
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UFMG |
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Brasil |
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GABINETE - GABINETE - REITORIA |
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Universidade Federal de Minas Gerais |
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