Missing value estimation for mixed-attribute data sets Zhu, Xiaofeng, Zhang, Shichao, Jin, Zhi, Zhang, Zili and Xu, Zhuoming 2011, Missing value estimation for mixed-attribute data sets, IEEE transactions on knowledge and data engineering, vol. 23, no. 1, pp. 110-121, doi: 10.1109/TKDE.2010.99. Attached Files Name Description MIMEType Size Downloads Title Missing value estimation for mixed-attribute data sets Author(s) Zhu, Xiaofeng Zhang, Shichao Jin, Zhi Zhang, Zili orcid.org/0000-0002-8721-9333 Xu, Zhuoming Journal name IEEE transactions on knowledge and data engineering Volume number 23 Issue number 1 Start page 110 End page 121 Total pages 12 Publisher IEEE Place of publication Piscataway, NJ Publication date 2011-01 ISSN 1041-4347 1558-2191 Keyword(s) classification data mining methodologies machine learning Summary Missing data imputation is a key issue in learning from incomplete data. Various techniques have been developed with great successes on dealing with missing values in data sets with homogeneous attributes (their independent attributes are all either continuous or discrete). This paper studies a new setting of missing data imputation, i.e., imputing missing data in data sets with heterogeneous attributes (their independent attributes are of different types), referred to as imputing mixed-attribute data sets. Although many real applications are in this setting, there is no estimator designed for imputing mixed-attribute data sets. This paper first proposes two consistent estimators for discrete and continuous missing target values, respectively. And then, a mixture-kernel-based iterative estimator is advocated to impute mixed-attribute data sets. The proposed method is evaluated with extensive experiments compared with some typical algorithms, and the result demonstrates that the proposed approach is better than these existing imputation methods in terms of classification accuracy and root mean square error (RMSE) at different missing ratios. Language eng DOI 10.1109/TKDE.2010.99 Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category C1 Refereed article in a scholarly journal HERDC collection year 2011 Copyright notice ©2011, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30033730 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 04 Apr 2011, 10:07:58 EST Tue, 12 Apr 2011, 11:46:26 EST Fri, 15 Apr 2011, 12:44:15 EST Mon, 18 Apr 2011, 15:33:36 EST Mon, 18 Apr 2011, 15:34:46 EST Mon, 16 Jun 2014, 17:41:35 EST Wed, 02 Jul 2014, 15:20:40 EST Tue, 03 Feb 2015, 12:46:13 EST Filtered Full Citation counts:   Cited 185 times in TR Web of Science Cited 230 times in Scopus Search Google Scholar Access Statistics: 900 Abstract Views, 5 File Downloads  -  Detailed Statistics Created: Mon, 04 Apr 2011, 10:07:46 EST by Sandra Dunoon