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A general stochastic clustering method for automatic cluster discovery

Pattern recognition, 2011-10, Vol.44 (10), p.2786-2799

2011

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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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LiNearN: A new approach to nearest neighbour density estimator

Pattern recognition, 2014-08, Vol.47 (8), p.2702-2720

2014

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Mass estimation

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

2013

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Learning Sparse Kernel Classifiers for Multi-Instance Classification

IEEE transaction on neural networks and learning systems, 2013-09, Vol.24 (9), p.1377-1389

2013

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IEEE_Xplore

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

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

2005

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

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

2006

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Kernel-based clustering via Isolation Distributional Kernel

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

2023

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Density-ratio based clustering for discovering clusters with varying densities

Pattern recognition, 2016-12, Vol.60, p.983-997

2016

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Isolation Forest

2008 Eighth IEEE International Conference on Data Mining, 2008, p.413-422

2008

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IEEE_Xplore

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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IEEE_Xplore

The impact of isolation kernel on agglomerative hierarchical clustering algorithms

Pattern recognition, 2023-07, Vol.139, p.109517, Article 109517

2023

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CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities

Pattern recognition, 2021-09, Vol.117, p.107977, Article 107977

2021

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Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest

IEEE transactions on geoscience and remote sensing, 2022, Vol.60, p.1-16

2022

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IEEE_Xplore

To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators

IEEE transactions on knowledge and data engineering, 2007-12, Vol.19 (12), p.1652-1665

2007

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IEEE_Xplore

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

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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

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

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

2022

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Improving Deep Forest by Screening

IEEE transactions on knowledge and data engineering, 2022-09, Vol.34 (9), p.4298-4312

2022

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IEEE_Xplore

Improving Deep Forest by Confidence Screening

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

2018

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IEEE_Xplore

Multi-Label Learning with Emerging New Labels

IEEE transactions on knowledge and data engineering, 2018-10, Vol.30 (10), p.1901-1914

2018

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IEEE_Xplore

Grouping points by shared subspaces for effective subspace clustering

Pattern recognition, 2018-11, Vol.83, p.230-244

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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A general stochastic clustering method for automatic cluster discovery

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

LiNearN: A new approach to nearest neighbour density estimator

Mass estimation

Learning Sparse Kernel Classifiers for Multi-Instance Classification

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

Density-ratio based clustering for discovering clusters with varying densities

Isolation Forest

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

The impact of isolation kernel on agglomerative hierarchical clustering algorithms

CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities

Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest

To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators

A Principled Distributional Approach to Trajectory Similarity Measurement and its Application to 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 Screening

Improving Deep Forest by Confidence Screening

Multi-Label Learning with Emerging New Labels

Grouping points by shared subspaces for effective subspace clustering

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

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