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On the evaluation of outlier detection and one-class classification: a comparative study of algorithms, model selection, and ensembles
It has been shown that unsupervised outlier detection methods can be adapted to the one-class classification problem (Janssens and Postma, in:...
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An experimental study of existing tools for outlier detection and cleaning in trajectories
Outlier detection and cleaning are essential steps in data preprocessing to ensure the integrity and validity of data analyses. This paper focuses on...
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Online boxplot derived outlier detection
Outlier detection is a widely used technique for identifying anomalous or exceptional events across various contexts. It has proven to be valuable in...
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CoMadOut—a robust outlier detection algorithm based on CoMAD
Unsupervised learning methods are well established in the area of anomaly detection and achieve state of the art performances on outlier datasets....
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Survey on extreme learning machines for outlier detection
In a two-class classification task, if the number of examples of one class (majority) is much greater than that of another class (minority), then the...
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Outlier detection using an ensemble of clustering algorithms
Outlier detection is an important research area in the field of machine learning and data science. The presence of outliers in a dataset limits its...
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A double-weighted outlier detection algorithm considering the neighborhood orientation distribution of data objects
Outlier detection is a hot research topic in data mining, and its requirements for algorithms to engage with various complex-shaped datasets more...
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Correlation-based outlier detection for ships’ in-service datasets
With the advent of big data, it has become increasingly difficult to obtain high-quality data. Solutions are required to remove undesired outlier...
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Discriminative boundary generation for effective outlier detection
Outlier detection is often considered a challenge due to the inherent class imbalance in datasets, with the small number of available outliers that...
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Outlier detection for incomplete real-valued data via information entropy and class-consistent technology
Outlier detection aims to find data points that are significantly different from other observed values. It has been widely used in fraud detection,...
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SA-O2DCA: Seasonal Adapted Online Outlier Detection and Classification Approach for WSN
Wireless Sensor Networks (WSNs) play a critical role in the Internet of Things by collecting information for real-world applications such as...
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Operational pattern forecast improvement with outlier detection in metro rail transport system
Transportation is an unavoidable part of every human’s life. The mobility system handles the transport of humans from different places using various...
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Towards a deep learning-based outlier detection approach in the context of streaming data
Uncommon observations that significantly vary from the norm are referred to as outliers. Outlier detection, which aims to detect unexpected behavior,...
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Fast, exact, and parallel-friendly outlier detection algorithms with proximity graph in metric spaces
In many fields, e.g., data mining and machine learning, distance-based outlier detection (DOD) is widely employed to remove noises and find abnormal...
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A data-adaptive method for outlier detection from functional data
Outliers present in a data set can severely impact the statistical analysis and lead to erroneous conclusions. Hence, outlier identification is an...
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Vision-based outlier detection techniques in automated surveillance: a survey and future ideas
Outlier detection is one of the emerging study topics influenced by video annotation. An outlier is anything odd or irregular that deviates from the...
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Solving imbalanced learning with outlier detection and features reduction
A critical problem for several real world applications is class imbalance. Indeed, in contexts like fraud detection or medical diagnostics, standard...
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OA-Net: outlier weakening and adaptive voxel encoding-based 3d object detection network
This paper focuses on the adverse impact of outlier points and the ambiguity of candidate localizations in 3D object detection in terms of point...
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Unsupervised outlier detection for time-series data of indoor air quality using LSTM autoencoder with ensemble method
The proposed framework consists of three modules as an outlier detection method for indoor air quality data. We first use a long short-term memory...
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Leveraging the Christoffel function for outlier detection in data streams
Outlier detection holds significant importance in the realm of data mining, particularly with the growing pervasiveness of data acquisition methods....
