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首页> 外文会议>Safety-critical Systems Symposium; 20060207-09; Bristol(GB) >Classification with Confidence for Critical Systems
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Classification with Confidence for Critical Systems

机译：关键系统的置信度分类

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摘要

In this paper we demonstrate an application of data-driven software development in a Bayesian framework such that every computed result arises from within a context and so can be associated with a confidence' estimate whose validity is underpinned by Bayesian principles. This technique, which induces software modules from data samples (e.g., training a neural network), can be contrasted with more traditional, abstract specification driven, software development that has tended to compute a result and then added secondary computation to produce an associated "confidence' measure. We demonstrate this approach applied to classification tasks — i.e., the challenge is to construct a software module that aims to classify its input vector as one of a number of potential target classes. Thus a series of features extracted from a mammogram (an input vector) might need to be classified as either tumour or non-tumour, in this case just two target classes. The set of classification probability estimates, which are fundamental to the Bayesian approach and constitute the "context' of any classification result, are generated by means of massive, but systematic, recomputation of results. We use state-of-the-art Reversible-Jump Markov Chain Monte Carlo (RJMCMC) methods to simulate the otherwise intractable integrals that emerge in applications of Bayes' Theorem. The focus of this paper is on "confidence' estimates as an integral part of classification software and on the role of such estimates in critical systems rather than on the recomputation techniques employed to get the results.
机译：在本文中，我们演示了数据驱动软件开发在贝叶斯框架中的应用，这样每个计算结果都来自上下文，因此可以与以贝叶斯原理为基础的置信度估计相关。该技术从数据样本中导出软件模块（例如，训练神经网络），可以与更传统的，抽象规范驱动的，倾向于计算结果然后添加二次计算以产生关联的“置信度”的软件开发进行对比。我们证明了这种方法适用于分类任务-即，挑战在于构建一个软件模块，该模块旨在将其输入向量分类为许多潜在目标类别之一。因此，从乳房X线照片（输入向量）可能需要分类为肿瘤还是非肿瘤，在这种情况下，仅是两个目标类别。分类概率估计集是贝叶斯方法的基础，构成任何分类结果的“上下文”，通过对结果进行大规模但系统的重新计算而产生的结果。我们使用最先进的可逆跳跃马尔可夫链蒙特卡洛（RJMCMC）方法来模拟在贝叶斯定理应用中出现的否则难以解决的积分。本文的重点是作为分类软件不可分割的一部分的“置信度”估计，以及此类估计在关键系统中的作用，而不是用于获得结果的重新计算技术。

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