Archetype analysis: A new subspace outlier detection approach

Fayos, Ismael Cabero; Epifanio, Irene; Pierola, Ana; Ballester, Alfredo

doi:10.1016/j.knosys.2021.106830

Cited by 12 publications

(4 citation statements)

References 69 publications

(112 reference statements)

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“…Keller et al [29] propose determining high-contrast subspaces for outlier detection as a form of data pre-processing. Cabero et al [30] also determine the subspaces as a data pre-processing step based on archetypal analysis followed by a k th NN approach. Some authors combine distance-based outlier detection with dimensionality reduction techniques such as principal component analysis [31] for high-dimensional data.…”

Section: Nearest Neighborsmentioning

confidence: 99%

A Probabilistic Transformation of Distance-Based Outliers

Muhr

Affenzeller

Küng

2023

MAKE

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The scores of distance-based outlier detection methods are difficult to interpret, and it is challenging to determine a suitable cut-off threshold between normal and outlier data points without additional context. We describe a generic transformation of distance-based outlier scores into interpretable, probabilistic estimates. The transformation is ranking-stable and increases the contrast between normal and outlier data points. Determining distance relationships between data points is necessary to identify the nearest-neighbor relationships in the data, yet most of the computed distances are typically discarded. We show that the distances to other data points can be used to model distance probability distributions and, subsequently, use the distributions to turn distance-based outlier scores into outlier probabilities. Over a variety of tabular and image benchmark datasets, we show that the probabilistic transformation does not impact outlier ranking (ROC AUC) or detection performance (AP, F1), and increases the contrast between normal and outlier score distributions (statistical distance). The experimental findings indicate that it is possible to transform distance-based outlier scores into interpretable probabilities with increased contrast between normal and outlier samples. Our work generalizes to a wide range of distance-based outlier detection methods, and, because existing distance computations are used, it adds no significant computational overhead.

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Section: Nearest Neighborsmentioning

confidence: 99%

A Probabilistic Transformation of Distance-Based Outliers

Muhr

Affenzeller

Küng

2023

MAKE

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“…Nguyen et al (2013) propose to find subspaces with strong mutual correlations and then identify anomalies on these subspaces. Finally, Cabero et al (2021) use archetype analysis to project the feature space into various subspaces with linear correlations based on nearest neighbours. On this basis, they explore outliers by ensembling the results obtained on relevant subspaces.…”

Section: Subspace-based Anomaly Detectionmentioning

confidence: 99%

Explainable contextual anomaly detection using quantile regression forests

Leeuwen

2023

Data Min Knowl Disc

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Traditional anomaly detection methods aim to identify objects that deviate from most other objects by treating all features equally. In contrast, contextual anomaly detection methods aim to detect objects that deviate from other objects within a context of similar objects by dividing the features into contextual features and behavioral features. In this paper, we develop connections between dependency-based traditional anomaly detection methods and contextual anomaly detection methods. Based on resulting insights, we propose a novel approach to inherently interpretable contextual anomaly detection that uses Quantile Regression Forests to model dependencies between features. Extensive experiments on various synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art anomaly detection methods in identifying contextual anomalies in terms of accuracy and interpretability.

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“…As a limitation, in the case of big data, the computation could be slow for archetypal-based methods. In that case, we could use more efficient algorithms, as explained in [39] (e.g., [37,[40][41][42][43]).…”

Section: Suitability Of Cf Tools In Classification Problems With Uncertaintiesmentioning

confidence: 99%

Combining Classification and User-Based Collaborative Filtering for Matching Footwear Size

et al. 2021

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Size mismatch is a serious problem in online footwear purchase because size mismatch implies an almost sure return. Not only foot measurements are important in selecting a size, but also user preference. This is the reason we propose several methodologies that combine the information provided by a classifier with anthropometric measurements and user preference information through user-based collaborative filtering. As novelties: (1) the information sources are 3D foot measurements from a low-cost 3D foot digitizer, past purchases and self-reported size; (2) we propose to use an ordinal classifier after imputing missing data with different options based on the use of collaborative filtering; (3) we also propose an ensemble of ordinal classification and collaborative filtering results; and (4) several methodologies based on clustering and archetype analysis are introduced as user-based collaborative filtering for the first time. The hybrid methodologies were tested in a simulation study, and they were also applied to a dataset of Spanish footwear users. The results show that combining the information from both sources predicts the foot size better and the new proposals provide better accuracy than the classic alternatives considered.

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Archetype analysis: A new subspace outlier detection approach

Cited by 12 publications

References 69 publications

A Probabilistic Transformation of Distance-Based Outliers

A Probabilistic Transformation of Distance-Based Outliers

Explainable contextual anomaly detection using quantile regression forests

Combining Classification and User-Based Collaborative Filtering for Matching Footwear Size

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