ABSTRACT Outlier detection has received special attention in various fields, mainly for those dealing with machine learning and artificial intelligence. As strong outliers, anomalies are divided into point, contextual and collective outliers. The most important challenges in outlier detection include the thin boundary between the remote points and natural area, the tendency of new data and noise to mimic the real data, unlabeled datasets and different definitions for outliers in different applications. Considering the stated challenges, we defined new types of anomalies called Collective Normal Anomaly and Collective Point Anomaly in order to improve a much better detection of the thin boundary between different types of anomalies. Basic domain-independent methods are introduced to detect these defined anomalies in both unsupervised and supervised datasets. The Multi-Layer Perceptron Neural Network is enhanced using the Genetic Algorithm to detect new defined anomalies with a higher precision so as to ensure a test error less than that be calculated for the conventional Multi-Layer Perceptron Neural Network. Experimental results on benchmark datasets indicated reduced error of anomaly detection process in comparison to baselines.

中文翻译：

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使用增强神经网络检测异常值检测中不同类型异常之间的细边界

摘要离群点检测在各个领域都受到了特别的关注，主要是那些涉及机器学习和人工智能的领域。作为强异常值，异常分为点异常值、上下文异常值和集体异常值。异常值检测中最重要的挑战包括远程点和自然区域之间的细边界、新数据和噪声模仿真实数据的趋势、未标记的数据集以及不同应用中对异常值的不同定义。考虑到上述挑战，我们定义了新的异常类型，称为集体正常异常和集体点异常，以便更好地检测不同类型异常之间的细边界。引入了基本的域独立方法来检测无监督和监督数据集中这些定义的异常。多层感知器神经网络使用遗传算法进行了增强，以更高的精度检测新定义的异常，以确保测试误差小于传统多层感知器神经网络的计算误差。基准数据集的实验结果表明，与基线相比，异常检测过程的错误减少了。