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024 7 _a10.1007/978-981-99-7657-7
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072 7 _aCOM021000
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082 0 4 _a005.7
_223
100 1 _aQi, Zhixin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_994453
245 1 0 _aDirty Data Processing for Machine Learning
_h[electronic resource] /
_cby Zhixin Qi, Hongzhi Wang, Zejiao Dong.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXIII, 133 p. 1 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter 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 _aIn 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 _aArtificial intelligence
_xData processing.
_921787
650 0 _aData mining.
_93907
650 0 _aBig data.
_94174
650 1 4 _aData Science.
_934092
650 2 4 _aData Mining and Knowledge Discovery.
_994456
650 2 4 _aBig Data.
_94174
700 1 _aWang, Hongzhi.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_94897
700 1 _aDong, Zejiao.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_994458
710 2 _aSpringerLink (Online service)
_994462
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819976560
776 0 8 _iPrinted edition:
_z9789819976584
776 0 8 _iPrinted edition:
_z9789819976591
856 4 0 _uhttps://doi.org/10.1007/978-981-99-7657-7
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cEBK
999 _c87061
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