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Mathematical Problems in Engineering
Volume 2015, Article ID 538613, 14 pages
Research Article

Missing Values and Optimal Selection of an Imputation Method and Classification Algorithm to Improve the Accuracy of Ubiquitous Computing Applications

1SKKU Business School, Sungkyunkwan University, Seoul 110734, Republic of Korea
2School of Management, Kyung Hee University, Seoul 130701, Republic of Korea

Received 18 June 2014; Revised 29 September 2014; Accepted 11 October 2014

Academic Editor: Jong-Hyuk Park

Copyright © 2015 Jaemun Sim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In a ubiquitous environment, high-accuracy data analysis is essential because it affects real-world decision-making. However, in the real world, user-related data from information systems are often missing due to users’ concerns about privacy or lack of obligation to provide complete data. This data incompleteness can impair the accuracy of data analysis using classification algorithms, which can degrade the value of the data. Many studies have attempted to overcome these data incompleteness issues and to improve the quality of data analysis using classification algorithms. The performance of classification algorithms may be affected by the characteristics and patterns of the missing data, such as the ratio of missing data to complete data. We perform a concrete causal analysis of differences in performance of classification algorithms based on various factors. The characteristics of missing values, datasets, and imputation methods are examined. We also propose imputation and classification algorithms appropriate to different datasets and circumstances.