"Missing is useful": Missing values in cost-sensitive decision trees

Zhang, S; Qin, Z; Ling, CX; Sheng, S

"Missing is useful": Missing values in cost-sensitive decision trees

Zhang, S Qin, Z Ling, CX Sheng, S

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Knowledge and Data Engineering, 2005, 17 (12), pp. 1689 - 1693
Issue Date:: 2005-12-01

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Field	Value	Language
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Qin, Z	en_US
dc.contributor.author	Ling, CX	en_US
dc.contributor.author	Sheng, S	en_US
dc.date.issued	2005-12-01	en_US
dc.identifier.citation	IEEE Transactions on Knowledge and Data Engineering, 2005, 17 (12), pp. 1689 - 1693	en_US
dc.identifier.issn	1041-4347	en_US
dc.identifier.uri	http://hdl.handle.net/10453/5790
dc.description.abstract	Many real-world data sets for machine learning and data mining contain missing values and much previous research regards it as a problem and attempts to impute missing values before training and testing. In this paper, we study this issue in cost-sensitive learning that considers both test costs and misclassification costs. If some attributes (tests) are too expensive in obtaining their values, it would be more cost-effective to miss out their values, similar to skipping expensive and risky tests (missing values) in patient diagnosis (classification). That is, "missing is useful" as missing values actually reduces the total cost of tests and misclassifications and, therefore, it is not meaningful to impute their values. We discuss and compare several strategies that utilize only known values and that "missing is useful" for cost reduction in cost-sensitive decision tree learning. © 2005 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Knowledge and Data Engineering	en_US
dc.relation.isbasedon	10.1109/TKDE.2005.188	en_US
dc.subject.classification	Information Systems	en_US
dc.title	"Missing is useful": Missing values in cost-sensitive decision trees	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	17	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	17	en_US

Abstract:

Many real-world data sets for machine learning and data mining contain missing values and much previous research regards it as a problem and attempts to impute missing values before training and testing. In this paper, we study this issue in cost-sensitive learning that considers both test costs and misclassification costs. If some attributes (tests) are too expensive in obtaining their values, it would be more cost-effective to miss out their values, similar to skipping expensive and risky tests (missing values) in patient diagnosis (classification). That is, "missing is useful" as missing values actually reduces the total cost of tests and misclassifications and, therefore, it is not meaningful to impute their values. We discuss and compare several strategies that utilize only known values and that "missing is useful" for cost reduction in cost-sensitive decision tree learning. © 2005 IEEE.

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http://hdl.handle.net/10453/5790