PyNomaly: Anomaly detection using Local Outlier Probabilities (LoOP).

Constantinou, Valentino

doi:10.21105/joss.00845

Cited by 12 publications

(7 citation statements)

References 3 publications

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“…To run the experiments conducted in this study, the Python Scikit-learn (version 0.23.0) implementations of OC-SVM and IF were used [56]. The Python library PyNomaly (version 0.3.3) was used for the implementation of the LoOP algorithm [57]. Finally, we implemented our own autoencoder with the Python library Keras (https://keras.io/, online accessed 8 December 2020) (version 2.3.0).…”

Section: Outlier Detection Algorithmsmentioning

confidence: 99%

Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series

Mouret

Albughdadi²,

Duthoit³

et al. 2021

Remote Sensing

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This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on rapeseed and wheat parcels located in Beauce (France). The proposed methodology consists of four sequential steps: (1) preprocessing of synthetic aperture radar (SAR) and multispectral images acquired using Sentinel-1 and Sentinel-2 satellites, (2) extraction of SAR and multispectral pixel-level features, (3) computation of parcel-level features using zonal statistics and (4) outlier detection. The different types of anomalies that can affect the studied crops are analyzed and described. The different factors that can influence the outlier detection results are investigated with a particular attention devoted to the synergy between Sentinel-1 and Sentinel-2 data. Overall, the best performance is obtained when using jointly a selection of Sentinel-1 and Sentinel-2 features with the isolation forest algorithm. The selected features are co-polarized (VV) and cross-polarized (VH) backscattering coefficients for Sentinel-1 and five Vegetation Indexes for Sentinel-2 (among us, the Normalized Difference Vegetation Index and two variants of the Normalized Difference Water). When using these features with an outlier ratio of 10%, the percentage of detected true positives (i.e., crop anomalies) is equal to 94.1% for rapeseed parcels and 95.5% for wheat parcels.

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Section: Outlier Detection Algorithmsmentioning

confidence: 99%

Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series

Mouret

Albughdadi²,

Duthoit³

et al. 2021

Remote Sensing

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“…To help approach these problems, established outlier detection packages exist in various programming languages such as ELKI Data Mining (Achtert et al, 2010) and RapidMiner (Hofmann and Klinkenberg, 2013) in Java and outliers (Komsta and Komsta, 2011) in R. However, Python, one of the most important languages in machine learning, still lacks a dedicated toolkit for outlier detection. Existing implementations either stand as single (Constantinou, 2018) or exist as part of a general-purpose framework like scikit-learn (Pedregosa et al, 2011) which does not cater specifically to anomaly detection. To fill this gap, we propose and implement PyOD-a comprehensive Python toolbox for scalable outlier detection.…”

Section: Introductionmentioning

confidence: 99%

PyOD: A Python Toolbox for Scalable Outlier Detection

Zhao,

Nasrullah,

2019

Preprint

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PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data. Uniquely, it provides access to a wide range of outlier detection algorithms, including established outlier ensembles and more recent neural network-based approaches, under a single, well-documented API designed for use by both practitioners and researchers. With robustness and scalability in mind, best practices such as unit testing, continuous integration, code coverage, maintainability checks, interactive examples and parallelization are emphasized as core components in the toolbox's development. PyOD is compatible with both Python 2 and 3 and can be installed through Python Package Index (PyPI) or https://github.com/yzhao062/pyod.

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“…LOF has a quadratic time complexity while all the tree-based methods demonstrate linear trends. LoOP is not included in this test because we use the PyNomaly [7] Python implementation while other methods are implemented in Matlab. LoOP is expected to have similar run time as LOF.…”

Section: Scale-up Testmentioning

confidence: 99%

Anomaly Detection via Neighbourhood Contrast

Chen

Ting

Chin

2020

Advances in Knowledge Discovery and Data Mining

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Relative scores such as Local Outlying Factor and mass ratio have been shown to be better scores than global scores in detecting anomalies. While this is true, our analysis reveals for the first time that these relative scores have a key shortcoming: anomalies have greatly different relative scores if they are located in different regions where the curvatures of the density surface are very different. As a result, the lowscore anomalies could be ranked lower than some normal points. This revelation motivates (i) a new score called Neighbourhood Contrast (NC) which produces approximately the same high scores for all anomalies, regardless of varying curvatures of the density surface in different regions; and (ii) an anomaly detection method based on NC. Our experiments show that the proposed method which employs the new score significantly outperforms methods using the aforementioned relative scores on benchmark datasets.

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PyNomaly: Anomaly detection using Local Outlier Probabilities (LoOP).

Abstract: Authors of papers retain copyright and release the work under a Creative Commons Attribution 4.0 International License (CC-BY).

Cited by 12 publications

References 3 publications

Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series

Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series

PyOD: A Python Toolbox for Scalable Outlier Detection

Anomaly Detection via Neighbourhood Contrast

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