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Isolation-Based Anomaly Detection

ACM transactions on knowledge discovery from data, 2012-03, Vol.6 (1), p.1-39

2012

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Feature-subspace aggregating: ensembles for stable and unstable learners

Machine learning, 2011-03, Vol.82 (3), p.375-397

2011

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Isolation‐based anomaly detection using nearest‐neighbor ensembles

Computational intelligence, 2018-11, Vol.34 (4), p.968-998

2018

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Local contrast as an effective means to robust clustering against varying densities

Machine learning, 2018-09, Vol.107 (8-10), p.1621-1645

2018

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Commentary: a decomposition of the outlier detection problem into a set of supervised learning problems

Machine learning, 2016-11, Vol.105 (2), p.301-304

2016

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Isolation Distributional Kernel A New Tool for Point & Group Anomaly Detection

IEEE transactions on knowledge and data engineering, 2021, p.1-1

2021

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Half-space mass: a maximally robust and efficient data depth method

Machine learning, 2015-09, Vol.100 (2-3), p.677-699

2015

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Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

Machine learning, 2012-02, Vol.86 (2), p.233-272

2012

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Mass estimation

Machine learning, 2013, Vol.90 (1), p.127-160

2013

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On Detecting Clustered Anomalies Using SCiForest

Machine Learning and Knowledge Discovery in Databases, p.274-290

Volltextzugriff (PDF)

Music classification via the bag-of-features approach

Pattern recognition letters, 2011-10, Vol.32 (14), p.1768-1777

2011

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On the application of ROC analysis to predict classification performance under varying class distributions

Machine learning, 2005, Vol.58 (1), p.25-32

2005

Volltextzugriff (PDF)

z-SVM: An SVM for Improved Classification of Imbalanced Data

AI 2006: Advances in Artificial Intelligence, 2006, Vol.4304, p.264-273

2006

Volltextzugriff (PDF)

Kernel-based clustering via Isolation Distributional Kernel

Information systems (Oxford), 2023-07, Vol.117, p.102212, Article 102212

2023

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Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel

The Journal of artificial intelligence research, 2021, Vol.71, p.667-695

2021

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Classifying under computational resource constraints : anytime classification using probabilistic estimators

Machine learning, 2007-10, Vol.69 (1), p.35-53

2007

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New Class Adaptation Via Instance Generation in One-Pass Class Incremental Learning

2017 IEEE International Conference on Data Mining (ICDM), 2017, p.1207-1212

2017

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A Principled Distributional Approach to Trajectory Similarity Measurement and its Application to Anomaly Detection

The Journal of artificial intelligence research, 2024-01, Vol.79, p.865-893

2024

Volltextzugriff (PDF)

LOCAL MODELS-THE KEY TO BOOSTING STABLE LEARNERS SUCCESSFULLY

Computational intelligence, 2013-05, Vol.29 (2), p.331-356

2013

Volltextzugriff (PDF)

Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020, p.198-206

2020

Volltextzugriff (PDF)

A new distributional treatment for time series and an anomaly detection investigation

Proceedings of the VLDB Endowment, 2022-07, Vol.15 (11), p.2321-2333

2022

Volltextzugriff (PDF)

Hierarchical clustering that takes advantage of both density-peak and density-connectivity

Information systems (Oxford), 2022-01, Vol.103, p.101871, Article 101871

2022

Volltextzugriff (PDF)

Improving Deep Forest by Confidence Screening

2018 IEEE International Conference on Data Mining (ICDM), 2018, p.1194-1199

2018

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A new simple and efficient density estimator that enables fast systematic search

Pattern recognition letters, 2019-05, Vol.122, p.92-98

2019

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Ensemble Selection for SuperParent-One-Dependence Estimators

AI 2005: Advances in Artificial Intelligence, 2005, Vol.3809, p.102-112

2005

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Isolation-Based Anomaly Detection

Feature-subspace aggregating: ensembles for stable and unstable learners

Isolation‐based anomaly detection using nearest‐neighbor ensembles

Local contrast as an effective means to robust clustering against varying densities

Commentary: a decomposition of the outlier detection problem into a set of supervised learning problems

Isolation Distributional Kernel A New Tool for Point & Group Anomaly Detection

Half-space mass: a maximally robust and efficient data depth method

Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

Mass estimation

On Detecting Clustered Anomalies Using SCiForest

Music classification via the bag-of-features approach

On the application of ROC analysis to predict classification performance under varying class distributions

z-SVM: An SVM for Improved Classification of Imbalanced Data

Kernel-based clustering via Isolation Distributional Kernel

Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel

Classifying under computational resource constraints : anytime classification using probabilistic estimators

New Class Adaptation Via Instance Generation in One-Pass Class Incremental Learning

A Principled Distributional Approach to Trajectory Similarity Measurement and its Application to Anomaly Detection

LOCAL MODELS-THE KEY TO BOOSTING STABLE LEARNERS SUCCESSFULLY

Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection

A new distributional treatment for time series and an anomaly detection investigation

Hierarchical clustering that takes advantage of both density-peak and density-connectivity

Improving Deep Forest by Confidence Screening

A new simple and efficient density estimator that enables fast systematic search

Ensemble Selection for SuperParent-One-Dependence Estimators

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