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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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Bibliographic Details
Main Authors: Qi, Zhixin, Wang, Hongzhi (Author), Dong, Zejiao (Author)
Format: eBook
Language:English
Published: Singapore Springer Nature Singapore 2024, 2024
Edition:1st ed. 2024
Subjects:
Artificial Intelligence / Data Processing
Big Data
Data Mining
Data Mining And Knowledge Discovery
Data Science
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • 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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