Anomaly detection by robust statistics

Rousseeuw, Peter J.; Hubert, Mia

doi:10.1002/widm.1236

Cited by 167 publications

(117 citation statements)

References 83 publications

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“…When using z-score for outlier detection, the outlier data are lied out of z-score. The z-score lies in between negative 2.5 and positive 2.5 (Rousseeuw & Hubert, (2017). These data are then dropped in the analysis as suggested by Orr et al, (1991).…”

Section: B Methods Of Data Analysismentioning

confidence: 99%

Determinant Of Loan Supply Behavior : The Case Of Regional Development Banks In East Indonesia

Jusni¹,

Aswan²

2017

Proceedings of the 2nd International Conference on Accounting, Management, and Economics 2017 (ICAME 2017)

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Section: B Methods Of Data Analysismentioning

confidence: 99%

Determinant Of Loan Supply Behavior : The Case Of Regional Development Banks In East Indonesia

Jusni¹,

Aswan²

2017

Proceedings of the 2nd International Conference on Accounting, Management, and Economics 2017 (ICAME 2017)

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“…In [42] it is presented a method for detecting anomalies through a neural network with controlled long-term memory (LSTM). The mean, median and M-score are often used in statistical tests for the detection of anomalies [43]. The aim of the study [44] was to model the structure and functioning of a complex information system that takes into account operators and users [45].…”

Section: Literature Reviewmentioning

confidence: 99%

The Capacity of the Road Network: Data Collection and Statistical Analysis of Traffic Characteristics

Shepelev

Aliukov²,

Nikolskaya³

et al. 2020

Energies

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The possibilities of collecting the necessary information using multi-touch cameras and ways to improve road traffic data collection are considered. An increase in the number of vehicles leads to traffic jams, which in turn leads to an increase in travel time, additional fuel consumption and other negative consequences. To solve this problem, it is necessary to have a reliable information collection system and apply modern effective methods of processing the collected information. The technology considered in the article allows taking into account pedestrians crossing the intersection. The purpose of this article is to determine the most important traffic characteristics that affect the traffic capacity of the intersection, in other words, the actual number of passing cars. Throughput is taken as a dependent variable. Based on the results of the regression analysis, a model was developed to predict the intersection throughput taking into account the most important traffic characteristics. Besides, this model is based on the fuzzy logic method and using the Fuzzy TECH 5.81d Professional Edition computer program.

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“…Peter J. Rousseeuw et al introduced to diagnose outliers by the first principal component score and its orthogonal distance of each data points. Regular observations have both a small orthogonal distance and small PCA score [14]. Heiko Hoffman proposes kernel PCA for novelty detection which is a non-linear extension of PCA.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Novelty Detection Using Deep Autoencoders with Density Based Clustering

2018

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Novelty detection is a classification problem to identify abnormal patterns; therefore, it is an important task for applications such as fraud detection, fault diagnosis and disease detection. However, when there is no label that indicates normal and abnormal data, it will need expensive domain and professional knowledge, so an unsupervised novelty detection approach will be used. On the other hand, nowadays, using novelty detection on high dimensional data is a big challenge and previous research suggests approaches based on principal component analysis (PCA) and an autoencoder in order to reduce dimensionality. In this paper, we propose deep autoencoders with density based clustering (DAE-DBC); this approach calculates compressed data and error threshold from deep autoencoder model, sending the results to a density based cluster. Points that are not involved in any groups are not considered a novelty; the grouping points will be defined as a novelty group depending on the ratio of the points exceeding the error threshold. We have conducted the experiment by substituting components to show that the components of the proposed method together are more effective. As a result of the experiment, the DAE-DBC approach is more efficient; its area under the curve (AUC) is shown to be 13.5 percent higher than state-of-the-art algorithms and other versions of the proposed method that we have demonstrated.

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Anomaly detection by robust statistics

Cited by 167 publications

References 83 publications

Determinant Of Loan Supply Behavior : The Case Of Regional Development Banks In East Indonesia

Determinant Of Loan Supply Behavior : The Case Of Regional Development Banks In East Indonesia

The Capacity of the Road Network: Data Collection and Statistical Analysis of Traffic Characteristics

Unsupervised Novelty Detection Using Deep Autoencoders with Density Based Clustering

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