Twala, B. E. T. H.; Jones, M. C. and Hand, D. J.
PDF (Accepted Manuscript)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
|DOI (Digital Object Identifier) Link:||http://doi.org/10.1016/j.patrec.2008.01.010|
|Google Scholar:||Look up in Google Scholar|
We propose a simple and effective method for dealing with missing data in decision trees used for classification. We call this approach 'missingness incorporated in attributes' (MIA). It is very closely related to the technique of treating 'missing' as a category in its own right, generalizing it for use with continuous as well as categorical variables. We show through a substantial data-based study of classification accuracy that MIA exhibits consistently good performance across a broad range of data types and of sources and amounts of missingness. It is competitive with the best of the rest (particularly, a multiple imputation EM algorithm method; EMMI) while being conceptually and computationally simpler. A simple combination of MIA and EMMI is slower but even more accurate.
|Item Type:||Journal Article|
|Copyright Holders:||2008 Elsevier B.V.|
|Keywords:||C4.5; CART; EM algorithm; fractional cases; missingness as attribute; multiple imputation|
|Academic Unit/Department:||Mathematics, Computing and Technology > Mathematics and Statistics
Mathematics, Computing and Technology
|Depositing User:||Sarah Frain|
|Date Deposited:||18 Aug 2010 10:32|
|Last Modified:||23 Feb 2016 17:32|
|Share this page:|
► Automated document suggestions from open access sources
Download history for this item
These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.