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Loss-Size and Reliability Trade-Offs Amongst Diverse Redundant Binary Classifiers

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Quantitative Evaluation of Systems (QEST 2020)

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Many applications involve the use of binary classifiers, including applications where safety and security are critical. The quantitative assessment of such classifiers typically involves receiver operator characteristic (ROC) methods and the estimation of sensitivity/specificity. But such techniques have their limitations. For safety/security critical applications, more relevant measures of reliability and risk should be estimated. Moreover, ROC techniques do not explicitly account for: 1) inherent uncertainties one faces during assessments, 2) reliability evidence other than the observed failure behaviour of the classifier, and 3) how this observed failure behaviour alters one’s uncertainty about classifier reliability. We address these limitations using conservative Bayesian inference (CBI) methods, producing statistically principled, conservative values for risk/reliability measures of interest. Our analyses reveals trade-offs amongst all binary classifiers with the same expected loss – the most reliable classifiers are those most likely to experience high impact failures. This trade-off is harnessed by using diverse redundant binary classifiers.

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    View this as the conditional expected loss, given the occurrence of the relevant error.

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    In [31] their focus was uncertainty about the value of the probability of failure for a system. Contrastingly, our result applies to the uncertainty about whether a given classifier will fail on its next classification task, and the loss incurred if it does.

  3. 3.

    From these, the expected loss for any of the adjudication functions may be computed.


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This work was supported by the European Commission through the H2020 programme under grant agreement 700692 (DiSIEM). My thanks to the anonymous reviewers for their helpful suggestions for improving the presentation.

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Correspondence to Kizito Salako .

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Salako, K. (2020). Loss-Size and Reliability Trade-Offs Amongst Diverse Redundant Binary Classifiers. In: Gribaudo, M., Jansen, D.N., Remke, A. (eds) Quantitative Evaluation of Systems. QEST 2020. Lecture Notes in Computer Science(), vol 12289. Springer, Cham.

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