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首页> 外文期刊>International Journal of Data Mining & Knowledge Management Process >Identification of Outliers in Oxazolines and Oxazoles High Dimension Molecular Descriptor Dataset Using Principal Component Outlier Detection Algorithm and Comparative Numerical Study of Other Robust Estimators
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Identification of Outliers in Oxazolines and Oxazoles High Dimension Molecular Descriptor Dataset Using Principal Component Outlier Detection Algorithm and Comparative Numerical Study of Other Robust Estimators

机译：使用主成分离群值检测算法和其他鲁棒估计量的比较数值研究，确定恶唑啉和恶唑高维分子描述符数据集中的离群值

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摘要

From the past decade outlier detection has been in use. Detection of outliers is an emerging topic and is having robust applications in medical sciences and pharmaceutical sciences. Outlier detection is used to detect anomalous behaviour of data. Typical problems in Bioinformatics can be addressed by outlier detection. A computationally fast method for detecting outliers is shown, that is particularly effective in high dimensions. PrCmpOut algorithm make use of simple properties of principal components to detect outliers in the transformed space, leading to significant computational advantages for high dimensional data. This procedure requires considerably less computational time than existing methods for outlier detection. The properties of this estimator (Outlier error rate (FN), Non-Outlier error rate(FP) and computational costs) are analyzed and compared with those of other robust estimators described in the literature through simulation studies. Numerical evidence based Oxazolines and Oxazoles molecular descriptor dataset shows that the proposed method performs well in a variety of situations of practical interest. It is thus a valuable companion to the existing outlier detection methods.
机译：从过去的十年开始，一直在使用离群值检测。离群值的检测是一个新兴的主题，并且在医学和制药科学中具有强大的应用。离群值检测用于检测数据的异常行为。生物信息学中的典型问题可以通过异常检测来解决。显示了一种用于检测离群值的计算快速方法，该方法在高维方面特别有效。 PrCmpOut算法利用主成分的简单属性来检测变换空间中的异常值，从而为高维数据带来了显着的计算优势。与用于离群值检测的现有方法相比，此过程所需的计算时间少得多。分析了该估计量的属性（异常值错误率（FN），非异常值错误率（FP）和计算成本），并通过仿真研究与文献中描述的其他鲁棒估计量进行了比较。基于恶唑啉和恶唑分子描述符数据集的数字证据表明，该方法在各种实际感兴趣的情况下都能很好地执行。因此，它是现有异常值检测方法的宝贵伴侣。

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