Affiliations: [a] College of Information Science and Engineering, Hunan Normal University, Changsha, Hunan, China
| [b] College of Mathematics, Hunan University, Changsha, Hunan, China
| [c] Shenzhen Research Institute of Hunan University, Shenzhen, Guangdong, China
Correspondence:
[*]
Corresponding author. Yu Fu and Mingjie Cai. E-mail: yu7bin@hotmail.com.
Abstract: Forecasting is making predictions about what will happen or how things will change. This can help people avoid blindness and losses and play a significant role in their lives. In multi-attribute prediction problems, the correlation between attributes is often ignored, which affects prediction accuracy. Based on fuzzy rough sets and logistic regression, this paper proposes a new logistic regression method that fully considers attribute correlation, namely a twin logistic regression method based on attribute-oriented fuzzy rough sets. Firstly, attribute-oriented fuzzy rough sets are studied and analyzed. Then, the optimistic and pessimistic predictions are achieved by fuzzy rough sets and logistic regression, and the final result is obtained by fusing the optimistic and pessimistic predictions. Finally, the effectiveness of the twin logistic regression method is verified.
Keywords: Attribute-oriented fuzzy rough set, logistic regression, twin logistic regression based on attribute-oriented fuzzy rough set, multi-attribute prediction
