FGRA: Toward Flexible Logic Mining with Ensemble Multi-Attribute Selection and Discrete Hopfield Neural Network

The logic mining model has gained significant attention with the advancement in processing complex and large-scale datasets. This advantage stems from the model’s capability to extract interpretable logical rules. However, many recent logic mining models fail to accurately retrieve optimal logic patterns from datasets because of their inflexible structures, rigid attribute selection, and fixed number of attributes. To address these issues, this paper proposes three innovative strategies through Flexible based G-Type Random 3-Satisfiability Reverse Analysis (FGRA) to enhance the performance of the induced logic in doing knowledge extraction and classification. First, FGRA utilizes G-Type Random 3-Satisfiability (GRAN3SAT) with the Discrete Hopfield Neural Network (DHNN) to diversify the clause of the logical rule. Later, the dataset is utilized in the DHNN training phase that incorporates GRAN3SAT. Second, FGRA adopts a cost function that maximizes classification accuracy and evaluates the effectiveness of the induced logic using multiple evaluation metrics in experiments. This procedure is vital to address the low performance of the potential GRAN3SAT logic before it enters the retrieval phase of the DHNN. Third, FGRA employs a multi-attribute selection ensemble approach, including log-linear selection, random selection, and correlation analysis, to identify key attributes for constructing the GRAN3SAT formulation. These strategies are crucial to ensure the model can adapt to the nature of the datasets based on varying sizes and complexity. The experimental results indicate that the performance of FGRA is promising compared to the other state-of-the-art logic mining models with an average accuracy of 0.873 in extracting the best-induced logic from numerous benchmark datasets. These findings have significant implications for bridging the gap between the logical rule and the data pattern, marking an important contribution to the field.