NDLI: A comparative study of missing value estimation methods: Which method performs better?

Content Provider	IEEE Xplore Digital Library
Author	Eng Aik Lim Zainuddin, Z.
Copyright Year	2008
Description	Author affiliation: Insitute of Eng. Mathematic, Univ. Malaysia Perlis, Kuala Perlis (Eng Aik Lim) \|\| Sch. of Math. Sci., Univ. Sains Malaysia, Minden (Zainuddin, Z.)
Abstract	Missing data is a problem that permeates much of the research bring done today. Some data frequently contain missing values such as gene expression data, which most of its down stream analyses for microarray experiments require complete data. In the literature many methods have been proposed to estimate missing values via information of the correlation patterns within the data matrix. In this report we describe an evaluation of top three current methods including a neural network method and two imputation methods on multiple types of data including microarray data, time series data such as air pollutant data and phytoplankton data. Based on the overall performance of the method, we then determine the most appropriate method that can be applied to various data sets. We found that the optimal method (local least square imputation (LLS) and Bayesian principle component analyses (BPCA)) are all highly competitive to each other in overall results. We tested with radial basis function (RBF) network method which is one of the neural network methods and found that, the overall performance of RBF network is lower than BPCA method and LLS method. According to the overall NRMSE of the three methods, the BPCA method provides the most accurate estimation for missing values.
Starting Page	1
Ending Page	5
File Size	2664064
Page Count	5
File Format	PDF
ISBN	9781424423156
DOI	10.1109/ICED.2008.4786656
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-01
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data analysis Bayesian methods Clustering methods Neural networks Clustering algorithms Air pollution Mathematics Gene expression Least squares methods Testing
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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