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

IEEE Computer Soc
Publication Type:
Journal Article
IEEE Transactions On Knowledge And Data Engineering, 2005, 17 (12), pp. 1689 - 1693
Issue Date:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2005000762.pdf969.99 kB
Adobe PDF
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-sensiti
Please use this identifier to cite or link to this item: