In recent years, outlier detection has attained great attention with machine learning
techniques due to its wide range of applications. By considering the input data's distributive …

Autoencoders were widely used in many machine learning tasks thanks to their strong
learning ability which has drawn great interest among researchers in the field of outlier …

In this paper, we introduce autoencoder ensembles for unsupervised outlier detection. One
problem with neural networks is that they are sensitive to noise and often require large data …

Outlier identification is one of the trending research projects, which is used to detect the
normal (important) and abnormal (abusive, unimportant, attack) content presented in the …

Outlier detection is one of the most frequently demanded task for optimizing results. Distance-
based methods are a popular approach. They require no prior assumptions about the data …

Although research on outlier detection methods has been an investigation area for long, few
of those studies relate to an Internet of Things (IoT) domain. Several critical decisions taken …

Aiming at the practical problems of outlier detection, such as unreasonable assumptions,
uncertain thresholds and repeated manual debugging, a novel method of outlier detection …

The high dimensionality and massive amount of data in open environments make the
existing low‐dimensional outlier detection methods time‐consuming. The support vector …

Recently, outlier detection has widespread applications in different areas. The task is to
identify outliers in the dataset and extract potential information. The existing outlier detection …

In recent years, due to the increase of researches based on data management, outlier
detection has attracted enormous interest. There is always a possibility of having outliers …