Missing Value Estimation for Mixed-Attribute Data Sets

• Published in: IEEE transactions on knowledge and data engineering Vol. 23; no. 1; pp. 110 - 121
• Main Authors: Zhu, Xiaofeng, Zhang, Shichao, Jin, Zhi, Zhang, Zili, Xu, Zhuoming
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.01.2011; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Bibliographies; Classification; Computer science; control theory; systems; Data mining; Data processing. List processing. Character string processing; Dealing; Estimators; Exact sciences and technology; Information science; Iterative algorithms; Iterative methods; Kernel; Learning; Machine learning; Machine learning algorithms; Mean square values; Memory organisation. Data processing; methodologies; Missing data; Operations research; Root mean square; Software; Studies; Data analysis; Methodology; methodologies; Iterative method; Data mining; machine learning; Mean square error; Kernel method; Missing data; Consistent estimator; Learning (artificial intelligence); Classification; Incomplete information; Artificial intelligence
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2010.99

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.