Enhancing Interpretability of Black-box Soft-margin SVM by Integrating Data-based Priors
This work addresses interpretability issues for practitioners using SVM models, but it appears incremental as it builds on existing methods with a specific enhancement.
The paper tackled the problem of interpretability in black-box soft-margin SVM models by integrating data-based priors, resulting in an interpretable or partly interpretable optimization model that was applied to eight benchmark examples to demonstrate effectiveness.
The lack of interpretability often makes black-box models difficult to be applied to many practical domains. For this reason, the current work, from the black-box model input port, proposes to incorporate data-based prior information into the black-box soft-margin SVM model to enhance its interpretability. The concept and incorporation mechanism of data-based prior information are successively developed, based on which the interpretable or partly interpretable SVM optimization model is designed and then solved through handily rewriting the optimization problem as a nonlinear quadratic programming problem. An algorithm for mining data-based linear prior information from data set is also proposed, which generates a linear expression with respect to two appropriate inputs identified from all inputs of system. At last, the proposed interpretability enhancement strategy is applied to eight benchmark examples for effectiveness exhibition.