S-Type Satisfiability Logic Mining for Medical Datasets

The logic mining approach has been explored extensively by various researchers. Nevertheless, current logic mining algorithms have neglected the significance of data preprocessing, resulting in a limited capacity to generalize the retrieved induced logic. Furthermore, present logic mining models have significant disadvantages, such as rigid logical structures. This work addresses the existing gap by proposing a new logic mining model. The model combines a supervised data preprocessing phase with the non-systematic Satisfiability of S-type Random 2 Satisfiability within a Discrete Hopfield Neural Network. The innovative dynamic-unit Discrete Hopfield Neural Network integrates a multi-objective function, which greatly improves the search space and results in optimal solutions. The medical dataset experiments and performance metrics indicate that the proposed model surpasses its counterparts.

Text 127621.pdf Restricted to Repository staff only Download (3MB) Official URL or Download Paper: https://www.taylorfrancis.com/chapters/edit/10.120...

Actions (login required)

View Item