Title: Enhancing Interpretability of Black-box Soft-margin SVM by Integrating Data-based Priors,
Abstract: 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.