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

Shaohan Chen; Chuanhou Gao; Ping Zhang

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arXiv · 1710.02924

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

Shaohan Chen·Chuanhou Gao·Ping Zhang

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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.

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BibTeX RIS

Shaohan Chen, Chuanhou Gao, Ping Zhang. 2019-02-02. Enhancing Interpretability of Black-box Soft-margin SVM by Integrating Data-based Priors. https://arxiv.org/abs/1710.02924

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