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 …

Outliers are the data points which deviate significantly from the majority of the data points.
Finding outliers is an important task in various applications, especially in data mining. The …

Modelling normal data is one of the major challenges in outlier detection. Deep learning has
been proven to be effective in modelling underlying distributions of input training data …

This paper proposes a new outlier detection approach that measures the degree of
outlierness for each instance in a given dataset. The proposed model utilizes a mass-based …

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 …

To enhance the accuracy and robustness of outlier detection, in this paper we propose an
outlier detection method based on the improved DPC algorithm and centrifugal factor …

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 …

We hypothesize that similar objects should have similar outlier scores. To the best of our
knowledge, all existing outlier detectors calculate the outlier score for each object …

Outlier detection (OD) is a crucial machine learning task with key applications in various
sectors such as security, finance, and healthcare. Preserving data privacy has been …

Feature selection places an important role in improving the performance of outlier detection,
especially for noisy data. Existing methods usually perform feature selection and outlier …