Dirty Data Processing for Machine Learning (Record no. 87061)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04240nam a22005415i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-981-99-7657-7 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730170559.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 231129s2024 si | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9789819976577 |
| -- | 978-981-99-7657-7 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 005.7 |
| 100 1# - AUTHOR NAME | |
| Author | Qi, Zhixin. |
| 245 10 - TITLE STATEMENT | |
| Title | Dirty Data Processing for Machine Learning |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XIII, 133 p. 1 illus. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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 can affect the accuracy of results. Accordingly, this book analyzes the impacts of dirty data and explores effective methods for dirty data processing. Although existing data cleaning methods improve data quality dramatically, the cleaning costs are still high. If we knew how dirty data affected the accuracy of machine learning models, we could clean data selectively according to the accuracy requirements instead of cleaning all dirty data, which entails substantial costs. However, no book to date has studied the impacts of dirty data on machine learning models in terms of data quality. Filling precisely this gap, the book is intended for a broad audience ranging from researchers inthe database and machine learning communities to industry practitioners. Readers will find valuable takeaway suggestions on: model selection and data cleaning; incomplete data classification with view-based decision trees; density-based clustering for incomplete data; the feature selection method, which reduces the time costs and guarantees the accuracy of machine learning models; and cost-sensitive decision tree induction approaches under different scenarios. Further, the book opens many promising avenues for the further study of dirty data processing, such as data cleaning on demand, constructing a model to predict dirty-data impacts, and integrating data quality issues into other machine learning models. Readers will be introduced to state-of-the-art dirty data processing techniques, and the latest research advances, while also finding new inspirations in this field. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| General subdivision | Data processing. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Wang, Hongzhi. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Dong, Zejiao. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-981-99-7657-7 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Singapore : |
| -- | Springer Nature Singapore : |
| -- | Imprint: Springer, |
| -- | 2024. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data mining. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Big data. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Science. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Mining and Knowledge Discovery. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Big Data. |
| 912 ## - | |
| -- | ZDB-2-SCS |
| 912 ## - | |
| -- | ZDB-2-SXCS |
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