Enhancing Interpretability of Black-box Soft-margin SVM by Integrating Data-based Priors

TL;DR

This paper introduces a method to improve the interpretability of black-box soft-margin SVMs by integrating data-based prior information, making the models more transparent and practical for real-world applications.

Contribution

It proposes a novel approach to incorporate data-based priors into SVMs, along with an algorithm to mine such priors from data, and demonstrates effectiveness on benchmark datasets.

Findings

Enhanced interpretability of SVMs demonstrated on benchmarks

Effective mining of linear prior information from data

Improved transparency without significant performance loss

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.