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Handling Missing Data Problems with Sampling Methods

Authors: 

Rima Houari, Achene Bounceur, A-Kamel Tari, Tahar Kechadi

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
Missing data cases are a problem in all types of statistical analyses and arise in almost all application domains. Several schemes have been studied in this paper to overcome the drawbacks produced by missing values in data mining tasks, one of the most well known is based on preprocessing, formerly known as imputation. In this work, we propose a new multiple imputation approach based on sampling techniques to handle missing values problems, in order to improve the quality and efficiency of data mining process. The proposed method is favorably compared with some imputation techniques and outperforms the existing approaches using an experimental benchmark on a large scale, waveform dataset taken from machine learning repository and different rate of missing values (till 95%).
Conference Name: 
International Conference on Advanced Networking, Distributed Systems and Applications (INDS 2014)
Proceedings: 
International Conference on Advanced Networking, Distributed Systems and Applications (INDS 2014)
Digital Object Identifer (DOI): 
10.na
Publication Date: 
16/06/2014
Conference Location: 
Algeria
Institution: 
National University of Ireland, Dublin (UCD)
Open access repository: 
No
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