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Novo método para melhorar a extração de dados desequilibrados multi-classe

Novo método para melhorar a extração de dados desequilibrados multi-classe

          
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About the Book

O desequilíbrio das classes é um dos problemas mais difíceis para as técnicas de extração de dados e de aprendizagem automática. Os dados em aplicações do mundo real têm frequentemente uma distribuição de classes desequilibrada. Isto ocorre quando a maioria dos exemplos pertence a uma classe maioritária e poucos exemplos pertencem a uma classe minoritária. Neste caso, os classificadores padrão tendem a classificar todos os exemplos como uma classe maioritária e a ignorar completamente a classe minoritária. Para este problema, os investigadores propuseram muitas soluções, tanto a nível dos dados como a nível algorítmico. A maioria dos esforços concentra-se em problemas de classe binária. No entanto, a classe binária não é o único cenário em que o problema do desequilíbrio de classes prevalece. No caso de conjuntos de dados multi-classe, é muito mais difícil definir as classes maioritária e minoritária. Assim, a classificação multi-classe em conjuntos de dados desequilibrados continua a ser um importante tópico de investigação. No nosso livro, propusemos uma nova abordagem baseada em SOMTE (Synthetic Minority Over-sampling TEchnique) e clustering que é capaz de lidar com o problema de dados desequilibrados envolvendo múltiplas classes. Implementámos a nossa abordagem utilizando ferramentas de aprendizagem automática de código aberto: Weka e RapidMiner.


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Product Details
  • ISBN-13: 9786206378488
  • Publisher: KS Omniscriptum Publishing
  • Publisher Imprint: Edicoes Nosso Conhecimento
  • Height: 229 mm
  • No of Pages: 72
  • Spine Width: 4 mm
  • Width: 152 mm
  • ISBN-10: 6206378489
  • Publisher Date: 24 Aug 2023
  • Binding: Paperback
  • Language: Portuguese
  • Returnable: N
  • Weight: 118 gr


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