BROWSE
Detail
Conference Paper
A Comparison of the Effects of Data Imputation Methods on Model Performance
Cited 10 time in 
Share 
Share
- Authors
- Wooyoung Kim, Wonwoong Cho, Jangho Choi, Jiyong Kim, Cheonbok Park, Jaegul Choo
- Issue Date
- 2019-02
- Citation
- International Conference on Advanced Communications Technology (ICACT) 2019, pp.592-599
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Missing values cause critical problems on training a prediction model. Various missing data imputation methods have been introduced to settle down the problem. However, the imputation accuracy obtained by the methods is insufficient to validate performance of prediction models. Thus, in this study, we compare (1) imputation accuracy from various imputation methods as well as (2) the effects of imputation methods on prediction accuracy, investigating a relationship between imputation accuracy and prediction accuracy. For the comparison, we use water quality data composed of the latest actual observational multi-sensor data from Daecheong Lake. We conduct several experiments to compare seven imputation methods including a state of the art method, and their effects on three distinct prediction models. Through quantitative comparison and analysis, we proved that it is necessary to consider both imputation accuracy and model prediction accuracy when choosing an imputation method.
- KSP Keywords
- Critical problems, Imputation methods, Missing Data Imputation, Missing values, Model performance, Multi-Sensor, Performance of prediction, Prediction accuracy, Quality data, Quantitative comparison, Water quality