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

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Main Authors: Eng Aik, Lim, Zarita, Zainuddin
Other Authors: ealim@unimap.edu.my
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineering (IEEE) 2009
Subjects:
Missing data
Radial basis function networks
Estimation theory
Bayes methods
Least squares approximations
Air pollutant data
Missing value estimation
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/7393
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spelling my.unimap-73932009-12-10T03:39:51Z A comparative study of missing value estimation methods: which method performs better? Eng Aik, Lim Zarita, Zainuddin ealim@unimap.edu.my Missing data Radial basis function networks Estimation theory Bayes methods Least squares approximations Air pollutant data Missing value estimation Link to publisher's homepage at http://ieeexplore.ieee.org 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. 2009-12-10T03:39:51Z 2009-12-10T03:39:51Z 2008-12-01 Working Paper p.1-5 978-1-4244-2315-6 http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=4786656 http://hdl.handle.net/123456789/7393 en Proceedings of the International Conference on Electronic Design (ICED 2008) Institute of Electrical and Electronics Engineering (IEEE)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Missing data
Radial basis function networks
Estimation theory
Bayes methods
Least squares approximations
Air pollutant data
Missing value estimation
spellingShingle Missing data
Radial basis function networks
Estimation theory
Bayes methods
Least squares approximations
Air pollutant data
Missing value estimation
Eng Aik, Lim
Zarita, Zainuddin
A comparative study of missing value estimation methods: which method performs better?
description Link to publisher's homepage at http://ieeexplore.ieee.org
author2 ealim@unimap.edu.my
author_facet ealim@unimap.edu.my
Eng Aik, Lim
Zarita, Zainuddin
format Working Paper
author Eng Aik, Lim
Zarita, Zainuddin
author_sort Eng Aik, Lim
title A comparative study of missing value estimation methods: which method performs better?
title_short A comparative study of missing value estimation methods: which method performs better?
title_full A comparative study of missing value estimation methods: which method performs better?
title_fullStr A comparative study of missing value estimation methods: which method performs better?
title_full_unstemmed A comparative study of missing value estimation methods: which method performs better?
title_sort comparative study of missing value estimation methods: which method performs better?
publisher Institute of Electrical and Electronics Engineering (IEEE)
publishDate 2009
url http://dspace.unimap.edu.my/xmlui/handle/123456789/7393
_version_ 1643788802339110912
score 13.18916

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