JSATRA: topology based logic mining using discrete hopfield neural network

Obtaining optimal logical rules and maintaining the explainability of the logical rules are two crucial issues in developing a logic mining model. To address these challenges, a novel symbolic logical rule namely J-type Random 2,3 Satisfiability was proposed to represent the attribute in the datasets. The proposed logical rule consists of second and third order clauses where the order of the clause is randomly generated. A well-built logical rule will be integrated with a Discrete Hopfield Neural Network and applied to real-life datasets. To achieve the selection of important attributes, Topological Data Analysis was utilized to capture the structural characteristics of the dataset. After the attribute selection, the permutation operator will be applied during the training phase to increase the solution space of the logic mining. In this context, the expanded search space will ultimately yield the optimal induced logic. The proposed logic mining model was evaluated using numerous real-life datasets from various fields of study. Experimental results demonstrate that the proposed model outperforms all state-of-the-art logic mining models, achieving an average accuracy of 0.8375 and superior performance in precision, F1-score, and Matthews’ Correlation Coefficient.