Regression learning based on incomplete relationships between attributes
| Autor: | Liu Yu, Li Xiuxiu, Xinhong Hei, Yan Ruiping, Zhao Jinwei, Zhenghao Shi, Longlei Dong |
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| Rok vydání: | 2018 |
| Předmět: |
Information Systems and Management
Generalization 02 engineering and technology computer.software_genre Machine learning 01 natural sciences Fuzzy logic Theoretical Computer Science 010104 statistics & probability Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Learning based 0101 mathematics Interpretability Mathematics Markov chain business.industry Regression analysis Regression Computer Science Applications Control and Systems Engineering 020201 artificial intelligence & image processing Data mining Artificial intelligence Construct (philosophy) business computer Software |
| Zdroj: | Information Sciences. 422:408-431 |
| ISSN: | 0020-0255 |
| DOI: | 10.1016/j.ins.2017.09.023 |
| Popis: | In recent years, machine learning researchers have focused on methods to construct flexible and interpretable regression models. However, the method of obtaining complete knowledge from incomplete and fuzzy prior knowledge and the trade-off between the generalization performance and the interpretability of the model are very important factors to consider. In this paper, we propose a new regression learning method. Complete relationships are obtained from the incomplete fuzzy relationships between attributes by using Markov logic networks [29] . The complete relationships are then applied to constrain the shape of the regression model in the optimization procedure to solve the trade-off problem. Finally, the benefits of our approach are illustrated on benchmark data sets and in real-world experiments. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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