Takagi-Sugeno Modeling of Incomplete Data for Missing Value Imputation With the Use of Alternate Learning
Missing values often occur in real-world datasets, which undermines the data integrity and reduces the reliability of data mining. In this paper, a method of Takagi-Sugeno (TS) fuzzy modeling for incomplete data is proposed and utilized to estimate missing values. Considering the difference of attri...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9083969/ |