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Dirty Data Processing for Machine Learning

In both the database and machine learning communities, data quality has become a serious issue which cannot be ignored. In this context, we refer to data with quality problems as “dirty data.” Clearly, for a given data mining or machine learning task, dirty data in both training and test datasets ca...

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Detalhes bibliográficos
Main Authors: Qi, Zhixin (Author), Wang, Hongzhi (Author), Dong, Zejiao (Author)
Autor Corporativo: SpringerLink (Online service)
Formato: Recurso Electrónico livro electrónico
Idioma:inglês
Publicado em: Singapore : Springer Nature Singapore : Imprint: Springer, 2024.
Edição:1st ed. 2024.
Assuntos:
Artificial intelligence > Data processing.
Data mining.
Big data.
Data Science.
Data Mining and Knowledge Discovery.
Big Data.
Acesso em linha:https://doi.org/10.1007/978-981-99-7657-7
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Sumário:
  • Chapter 1. Introduction
  • Chapter 2. Impacts of Dirty Data on Classification and Clustering Models
  • Chapter 3. Dirty-Data Impacts on Regression Models
  • Chapter 4. Incomplete Data Classification with View-Based Decision Tree
  • Chapter 5. Density-Based Clustering for Incomplete Data
  • Chapter 6. Feature Selection on Inconsistent Data
  • Chapter 7. Cost-Sensitive Decision Tree Induction on Dirty Data.

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