Machine Learning

Enhancing Transparency of Black-box Soft-margin SVM by Integrating Data-based Prior Information

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  • arXiv
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    Enhancing Transparency of Black-box Soft-margin SVM by Integrating Data-based Prior Information

    The lack of transparency often makes the 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 transparency. The concept and incorporation mechanism of data-based prior information are successively developed, based on which the transparent or partly transparent 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 transparency strategy is applied to eight benchmark examples and two real blast furnace examples for effectiveness exhibition.

    Enhancing Transparency of Black-box Soft-margin SVM by Integrating Data-based Prior Information
    by Shaohan Chen, Chuanhou Gao, Ping Zhang
    https://arxiv.org/pdf/1710.02924v1.pdf

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