An improved one-class support vector machine classifier for outlier detection

An, Wenjuan; Liang, Mangui; Liu, He

doi:10.1177/0954406214537475

Cited by 10 publications

(6 citation statements)

References 26 publications

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“…When they compared the proposed approach against other methods, such as OC-SVM and ROCSVM, the results showed that their system presented the best performance within an acceptable false-positive rate. Another research [26] proposed a special case that tried to overcome the OC-SVM shortcoming of ignoring the training data's inner-class structure. The proposed method attempted to minimize the scatteredness of the training points; hence, the points can be easily separated from the origin.…”

Section: Related Workmentioning

confidence: 99%

Variational Autoencoders and Wasserstein Generative Adversarial Networks for Improving the Anti-Money Laundering Process

2021

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There has been much recent work on fraud and Anti Money Laundering (AML) detection using machine learning techniques. However, most algorithms are based on supervised techniques. Studies show that supervised techniques often have the limitation of not adapting well to new irregular fraud patterns when the dataset is highly imbalanced. Instead, unsupervised learning can have a better capability to find anomalous and irregular patterns in new transaction. Despite this, unsupervised techniques also have the disadvantage of not being able to give state-of-the-art detection results. We propose a suite of unsupervised and deep learning techniques to implement an anti-money laundering and fraud detection system to resolve this limitation. The system leverages three deep learning models: autoencoder (AE), variational autoencoder (VAE), and a generative adversarial network. We preprocess the given dataset to separate the Transaction Date attribute into its base components to capture time-related fraud patterns. Also, Wasserstein Generative Adversarial Network (WGAN) is used to generate fraud transactions, which are then mixed with the base dataset to form a more balanced mixed dataset. These two datasets are used to train the AE and VAE models. We built two versions of the AE model (single-loss and multi-loss) besides a novel method of calculating the anomaly score threshold, called Recall-First Threshold (RFT), which helps enhance the model's performance. Experimental results demonstrated that the False Positive Rate (FPR) drops down to as low as 7% in the proposed multi-loss AE model. In comparison, we achieved an accuracy of 93%, with 100% of the fraud transactions recalled successfully.

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Section: Related Workmentioning

confidence: 99%

Variational Autoencoders and Wasserstein Generative Adversarial Networks for Improving the Anti-Money Laundering Process

2021

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“…Amer presented eta one-class SVM to deal with the sensitivity of one-class SVM for outlier data [98]. An improved oneclass SVM ''OC-SVM'' that exploits the inner-class structure of the training set via minimizing the within-class scatter of the training data [100].…”

Section: Anomaly Detectionmentioning

confidence: 99%

Big data analytics for preventive medicine

Razzak

Imran

2019

Neural Comput & Applic

173

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Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations.

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“…In [14], an adaptive distributed Bayesian approach was proposed. Similarly, some improved SVM classifier methods also solved the outlier detection problems [15,16]. Clustering approaches have relied on similarity metrics to detect outliers [17,18].…”

Section: Introductionmentioning

confidence: 99%

A rapid outliers detection and correction method for external ballistic velocity measurement data

Yan,

Qian,

et al. 2023

Meas. Sci. Technol.

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Due to the inﬂuence of various sudden and abnormal factors during the tracking environment, equipment working conditions, and operation process, there are inevitable outliers in the tracking measurement process of aircraft such as carrier rockets, artiﬁcial satellites, and missiles. Therefore, the prerequisite for ensuring the reliability of processing results is to timely and accurately detect and correct outliers. In this paper, we proposed the sliding window-based variable degree B-spline function method (SWVD B-spline), which can handle isolated outliers and spotted outliers. SWVD B-spline uses variable degree B-spline function to model observed data in sliding windows, which can detect and correct outliers point by point along with window sliding. Then, we propose an initial window data selection method to remove outliers in initial windows to ensure the processing eﬀect. In addition, because there are often inﬂection points in external ballistic velocity measurement data, diﬀerential evolution is used to optimize variable degree B-spline in windows that include inﬂection points to improve processing accuracy. The experimental results verify that SWVD B-spline can handle various outliers rapidly and eﬃciently.

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An improved one-class support vector machine classifier for outlier detection

Cited by 10 publications

References 26 publications

Variational Autoencoders and Wasserstein Generative Adversarial Networks for Improving the Anti-Money Laundering Process

Variational Autoencoders and Wasserstein Generative Adversarial Networks for Improving the Anti-Money Laundering Process

Big data analytics for preventive medicine

A rapid outliers detection and correction method for external ballistic velocity measurement data

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