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首页> 外文期刊>International Journal of Hybrid Intelligent Systems >Bayesian anomaly detection and classification for noisy data
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Bayesian anomaly detection and classification for noisy data

机译：贝叶斯异常检测和噪声数据分类

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摘要

Statistical uncertainties are rarely incorporated into machine learning algorithms, especially for anomaly detection. Here we present the Bayesian Anomaly Detection And Classification (BADAC) formalism, which provides a unified statistical approach to classification and anomaly detection within a hierarchical Bayesian framework. BADAC deals with uncertainties by marginalising over the unknown, true, value of the data. Using simulated data with Gaussian noise as an example, BADAC is shown to be superior to standard algorithms in both classification and anomaly detection performance in the presence of uncertainties. Additionally, BADAC provides well-calibrated classification probabilities, valuable for use in scientific pipelines. We show that BADAC can work in online mode and is fairly robust to model errors, which can be diagnosed through model-selection methods. In addition it can perform unsupervised new class detection and can naturally be extended to search for anomalous subsets of data. BADAC is therefore ideal where computational cost is not a limiting factor and statistical rigour is important. We discuss approximations to speed up BADAC, such as the use of Gaussian processes, and finally introduce a new metric, the Rank-Weighted Score (RWS), that is particularly suited to evaluating an algorithm's ability to detect anomalies.
机译：统计不确定性很少被纳入机器学习算法，特别是对于异常检测。在这里，我们介绍了贝叶斯异常检测和分类（Badac）形式主义，为分层贝叶斯框架内的分类和异常检测提供了统一的统计方法。 Badac通过边缘化的不确定，真实，价值的数据的边缘化来涉及不确定性。使用具有高斯噪声的模拟数据作为示例，在存在不确定性的情况下，BADAC显示出在分类和异常检测性能中优于标准算法。此外，Badac提供良好校准的分类概率，有价值的科学管道。我们表明Badac可以在线模式工作，并且对模型错误相当强大，可以通过模型选择方法诊断。此外，它可以执行无监督的新类检测，并且可以自然地扩展以搜索数据的异常子集。因此，Badac是理想的，在计算成本不是限制因素，统计严格是重要的。我们讨论乘以加速BADAC的近似，例如使用高斯过程，最后引入新的公制，秩加权分数（RWS），特别适合评估算法检测异常的能力。

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  • 作者

    Ethan Roberts; Bruce A. Bassett; Michelle Lochner;

  • 作者单位

    University of Cape Town Rondebosch Cape Town South Africa African Institute of Mathematical Sciences Muizenburg Cape Town South Africa;

    University of Cape Town Rondebosch Cape Town South Africa African Institute of Mathematical Sciences Muizenburg Cape Town South Africa South African Radio Astronomical Observatory Observatory Cape Town South Africa South African Astronomical Observatory Observatory Cape Town South Africa;

    African Institute of Mathematical Sciences Muizenburg Cape Town South Africa South African Radio Astronomical Observatory Observatory Cape Town South Africa;

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  • 正文语种 eng
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  • 关键词

    Machine learning; anomalies; classification; novelty; Bayesian; unsupervised class detection;

    机译：机器学习;异常;分类;新奇;贝叶斯;无监督的课程检测;

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