A class center based approach for missing value imputation
| Autor: | Miao Ling Li, Wei Chao Lin, Chih-Fong Tsai |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Information Systems and Management Computer science 02 engineering and technology computer.software_genre Management Information Systems Support vector machine 03 medical and health sciences 030104 developmental biology Artificial Intelligence Data_GENERAL 0202 electrical engineering electronic engineering information engineering Missing value imputation 020201 artificial intelligence & image processing Imputation (statistics) Data mining computer Categorical variable Software |
| Zdroj: | Knowledge-Based Systems. 151:124-135 |
| ISSN: | 0950-7051 |
| DOI: | 10.1016/j.knosys.2018.03.026 |
| Popis: | Missing value imputation (MVI) is the major solution method for dealing with incomplete dataset problems in which the missing attribute values are replaced from a chosen set of observed data using some statistical methods, such as mean/mode, machine learning, or support vector machine methods. Although machine learning MVI approaches may produce reasonably good imputation results, they usually require larger imputation times than statistical approaches. In this paper, a Class Center based Missing Value Imputation (CCMVI) approach is introduced for producing effective imputation results more efficiently. It is based on measuring the class center of each class and then the distances between it and the other observed data are used to define a threshold for the later imputation. The experimental results based on numerical, categorical, and mixed data types of datasets show that the proposed CCMVI approach outperforms the other MVI approaches for both numerical and mixed datasets. In addition, it requires much less imputation time than the machine learning MVI methods. |
| Databáze: | OpenAIRE |
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