Roslan Armina
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Roslan Armina has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Azlan Mohd Zain
Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Malaysia
1 shared publication - Nor Azizah Ali
Universiti Teknologi Malaysia
1 shared publication - Roselina Sallehuddin
Universiti Teknologi Malaysia
1 shared publication
Latest Publications
- Open Access
- 21 September 2017
- Published by IOP Publishing in Journal of Physics: Conference Series
- Vol. 892 (1) , 012004
A Review On Missing Value Estimation Using Imputation Algorithm
The presence of the missing value in the data set has always been a major problem for precise prediction. The method for imputing missing value needs to minimize the effect of incomplete data sets for the prediction model. Many algorithms have been proposed for countermeasure of missing value problem. In this review, we provide a comprehensive analysis of existing imputation algorithm, focusing on the technique used and the implementation of global or local information of data sets for missing value estimation. In addition validation method for imputation result and way to measure the performance of imputation algorithm also described. The objective of this review is to highlight possible improvement on existing method and it is hoped that this review gives reader better understanding of imputation method trend.
- 21 September 2017
- Published by IOP Publishing in Journal of Physics: Conference Series
- Vol. 892 (1) , 012004
Open AccessA Review On Missing Value Estimation Using Imputation Algorithm
The presence of the missing value in the data set has always been a major problem for precise prediction. The method for imputing missing value needs to minimize the effect of incomplete data sets for the prediction model. Many algorithms have been proposed for countermeasure of missing value problem. In this review, we provide a comprehensive analysis of existing imputation algorithm, focusing on the technique used and the implementation of global or local information of data sets for missing value estimation. In addition validation method for imputation result and way to measure the performance of imputation algorithm also described. The objective of this review is to highlight possible improvement on existing method and it is hoped that this review gives reader better understanding of imputation method trend.