Interpretable Multiattribute Predictive Analysis Model Based on Rough Fuzzy Sets and Logistic Regression

Abstract

The interpretability of prediction models is very important for decision management. The vast majority of existing prediction analysis models based on rough set theory lack a certain interpretability. To break through the existing framework, this paper deeply integrates rough fuzzy sets and logistic regression to construct an interpretable prediction model for multi-attribute information systems. Firstly, the Jensen-Shannon divergence with statistical interpretability is used to capture the attribute information with strong correlation under on a level, and then an fresh attribute-oriented <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula> -rough fuzzy set model is presented. The pessimistic and optimistic decision concepts of the proposed rough fuzzy set model are used to to interpretability enhancement of data information. On this basis, an interpretable predictive analysis model is constructed by combining logistic regression model. The construction process of the prediction model is supported by sufficient interpretable information. The predicted result is a result of development trend with a certain interpretability. Finally, to assess the effectiveness and viability of the proposed prediction model, we conduct comparative experiments with other three prediction models accompanied with four prediction performance indexes. Experimental results display that the proposed prediction model has well-pleasing prediction performance and anti-noise ability.