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Melhorar os algoritmos de ML para conjuntos de dados desequilibrados
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ISBN10: 6208901553
ISBN13: 9786208901554
Publisher: Edicoes Nosso Conhecimento
Published: May 27 2025
Pages: 68
Weight: 0.23
Height: 0.16 Width: 6.00 Depth: 9.00
Language: Portuguese
ISBN13: 9786208901554
Publisher: Edicoes Nosso Conhecimento
Published: May 27 2025
Pages: 68
Weight: 0.23
Height: 0.16 Width: 6.00 Depth: 9.00
Language: Portuguese
A grande quantidade de dados gerados em linha permitiu que os cientistas de dados analisassem esta informação e tirassem conclusões em vários domÃnios. No entanto, os dados em tempo real são muitas vezes propensos a desequilÃbrios, o que pode degradar a qualidade dos dados e constitui um desafio significativo no domÃnio da aprendizagem automática. As técnicas baseadas em amostragem e os modelos baseados em algoritmos são dois métodos principais utilizados para resolver e equilibrar o desequilÃbrio dos dados. Esta tese apresenta três técnicas distintas para gerir diferentes nÃveis de desequilÃbrio em dados em tempo real.A abordagem inicial propõe uma técnica baseada em amostragem integrada com o mecanismo de ensacamento para tratar o desequilÃbrio dos dados. O modelo identifica o desequilÃbrio de dados baseado em classes e efectua uma sobreamostragem para cada classe disponÃvel. O mecanismo de ensacamento envolve a criação de subconjuntos dos dados de treino, com o objetivo de variar os nÃveis de desequilÃbrio nos dados de treino para garantir uma previsão eficaz. Apesar disso, o efeito do desequilÃbrio persiste no mecanismo de previsão, levando à classificação incorrecta de várias classes minoritárias.
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