DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles

Published in

arXiv.org(2019)

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TL;DR

An unsupervised outlier detector combination framework called DCSO is proposed, demonstrated and assessed for the dynamic selection of most competent base detectors, with an emphasis on data locality.

Abstract

Selecting and combining the outlier scores of different base detectors used within outlier ensembles can be quite challenging in the absence of ground truth. In this paper, an unsupervised outlier detector combination framework called DCSO is proposed, demonstrated and assessed for the dynamic selection of most competent base detectors, with an emphasis on data locality. The proposed DCSO framework first defines the local region of a test instance by its k nearest neighbors and then identifies the top-performing base detectors within the local region. Experimental results on ten benchmark datasets demonstrate that DCSO provides consistent performance improvement over existing static combination approaches in mining outlying objects. To facilitate interpretability and reliability of the proposed method, DCSO is analyzed using both theoretical frameworks and visualization techniques, and presented alongside empirical parameter setting instructions that can be used to improve the overall performance.

Authors

Yue Zhao

11 papers

References39 items

XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning

Dynamic classifier selection: Recent advances and perspectives

Discovering Communities and Anomalies in Attributed Graphs

Modeling Temporal Activity to Detect Anomalous Behavior in Social Media

Outlier Ensembles - An Introduction

DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles

Published in

arXiv.org(2019)

External Links:

Generate Graph

TL;DR

Abstract

Authors

Yue Zhao

11 papers

References39 items

XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning

Dynamic classifier selection: Recent advances and perspectives

Discovering Communities and Anomalies in Attributed Graphs

Modeling Temporal Activity to Detect Anomalous Behavior in Social Media

Outlier Ensembles - An Introduction

Incorporating Expert Feedback into Active Anomaly Discovery

Local subspace-based outlier detection using global neighbourhoods

Sequential Ensemble Learning for Outlier Detection: A Bias-Variance Perspective

On Clustering Time Series Using Euclidean Distance and Pearson Correlation

Model selection for anomaly detection

Theoretical Foundations and Algorithms for Outlier Ensembles

Learning Representations for Outlier Detection on a Budget

Less is More: Building Selective Anomaly Ensembles

Dynamic selection of classifiers - A comprehensive review

Ensembles for unsupervised outlier detection: challenges and research questions a position paper

Outlier ensembles: position paper

Fast and reliable anomaly detection in categorical data

LoOP: local outlier probabilities

From dynamic classifier selection to dynamic ensemble selection

Statistical Comparisons of Classifiers over Multiple Data Sets

Feature bagging for outlier detection

A theoretical framework for dynamic classifier selection

Multiple Classifier Systems

LOF: identifying density-based local outliers

A decision-theoretic generalization of on-line learning and an application to boosting

Bagging Predictors

Combination of multiple classifiers using local accuracy estimates

Stacked generalization

Individual Comparisons by Ranking Methods

The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance

An Ensemble Approach for Event Detection and Characterization in Dynamic Graphs

Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection

Visualizing Data using t-SNE

Decision Combination in Multiple Classifier Systems

Distribution-free Multiple Comparisons

Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Fast Approximate Nearest-Neighbor Search with k-Nearest Neighbor Graph

Maximum-of-Average ( SG_MOA ): after dividing base detectors into subgroups, taking the average score for each subgroup as the subgroup score

used both theoretical and practical explanations

Field of Study

Computer ScienceMathematics

Journal Information

Name

ArXiv

Volume

abs/2005.00687

Venue Information

Name

arXiv.org

Type

URL

https://arxiv.org

Alternate Names

ArXiv

TL;DR

Abstract

Authors

References39 items

XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning

Dynamic classifier selection: Recent advances and perspectives

Discovering Communities and Anomalies in Attributed Graphs

Modeling Temporal Activity to Detect Anomalous Behavior in Social Media

Outlier Ensembles - An Introduction

TL;DR

Abstract

Authors

References39 items

XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning

Dynamic classifier selection: Recent advances and perspectives

Discovering Communities and Anomalies in Attributed Graphs

Modeling Temporal Activity to Detect Anomalous Behavior in Social Media

Outlier Ensembles - An Introduction

Incorporating Expert Feedback into Active Anomaly Discovery

Local subspace-based outlier detection using global neighbourhoods

Sequential Ensemble Learning for Outlier Detection: A Bias-Variance Perspective

On Clustering Time Series Using Euclidean Distance and Pearson Correlation

Model selection for anomaly detection

Theoretical Foundations and Algorithms for Outlier Ensembles

Learning Representations for Outlier Detection on a Budget

Less is More: Building Selective Anomaly Ensembles

Dynamic selection of classifiers - A comprehensive review

Ensembles for unsupervised outlier detection: challenges and research questions a position paper

Outlier ensembles: position paper

Fast and reliable anomaly detection in categorical data

LoOP: local outlier probabilities

Isolation Forest

From dynamic classifier selection to dynamic ensemble selection

Statistical Comparisons of Classifiers over Multiple Data Sets

Feature bagging for outlier detection

A theoretical framework for dynamic classifier selection

Multiple Classifier Systems

LOF: identifying density-based local outliers

A decision-theoretic generalization of on-line learning and an application to boosting

Bagging Predictors

Combination of multiple classifiers using local accuracy estimates

Stacked generalization

Individual Comparisons by Ranking Methods

The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance

An Ensemble Approach for Event Detection and Characterization in Dynamic Graphs

Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection

Visualizing Data using t-SNE

Decision Combination in Multiple Classifier Systems

Distribution-free Multiple Comparisons

Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Fast Approximate Nearest-Neighbor Search with k-Nearest Neighbor Graph

Maximum-of-Average ( SG_MOA ): after dividing base detectors into subgroups, taking the average score for each subgroup as the subgroup score

used both theoretical and practical explanations

Field of Study

Journal Information

Name

Volume

Venue Information

Name

Type

URL

Alternate Names