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

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...