Colin Bellinger

According to our database, Colin Bellinger authored at least 44 papers between 2010 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Automated imbalanced classification via layered learning.

Vítor Cerqueira, Luís Torgo, Paula Branco, Colin Bellinger

Mach. Learn., June, 2023

Learning Visual Tracking and Reaching with Deep Reinforcement Learning on a UR10e Robotic Arm.

Colin Bellinger, Laurence Lamarche-Cliche

CoRR, 2023

Learning when to observe: A frugal reinforcement learning framework for a high-cost world.

Colin Bellinger, Mark Crowley, Isaac Tamblyn

CoRR, 2023

ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry.

Chris Beeler, Sriram Ganapathi Subramanian, Kyle Sprague, Nouha Chatti, Colin Bellinger, Mitchell Shahen, Nicholas Paquin, Mark Baula, Amanuel Dawit, Zihan Yang, Xinkai Li, Mark Crowley, Isaac Tamblyn

CoRR, 2023

Reinforcement Learning-based Wavefront Sensorless Adaptive Optics Approaches for Satellite-to-Ground Laser Communication.

Payam Parvizi, Runnan Zou, Colin Bellinger, Ross Cheriton, Davide Spinello

CoRR, 2023

An Interpretable Measure of Dataset Complexity for Imbalanced Classification Problems.

Jonatan Møller Nuutinen Gøttcke, Colin Bellinger, Paula Branco, Arthur Zimek

Proceedings of the 2023 SIAM International Conference on Data Mining, 2023

Efficient Augmentation for Imbalanced Deep Learning.

Damien A. Dablain, Colin Bellinger, Bartosz Krawczyk, Nitesh V. Chawla

Proceedings of the 39th IEEE International Conference on Data Engineering, 2023

Time and temporal abstraction in continual learning: tradeoffs, analogies and regret in an active measuring setting.

Vincent Létourneau, Colin Bellinger, Isaac Tamblyn, Maia Fraser

Proceedings of the Conference on Lifelong Learning Agents, 2023

2022

Interpretable ML for Imbalanced Data.

Damien A. Dablain, Colin Bellinger, Bartosz Krawczyk, David W. Aha, Nitesh V. Chawla

CoRR, 2022

Understanding CNN Fragility When Learning With Imbalanced Data.

Damien Dablain, Kristen N. Jacobson, Colin Bellinger, Mark Roberts, Nitesh V. Chawla

CoRR, 2022

4th Workshop on Deep Learning Practice and Theory for High-Dimensional Sparse and Imbalanced Data with KDD 2022.

Roberto Corizzo, Junfeng Ge, Colin Bellinger, Xiaoqiang Zhu, Paula Branco, Kuang-chih Lee, Nathalie Japkowicz, Ruiming Tang, Tao Zhuang, Han Zhu, Biye Jiang, Jiaxin Mao, Weinan Zhang

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Imbalanced Multi-layer Cloud Classification with Advanced Baseline Imager (ABI) and CloudSat/CALIPSO Data.

Leah Ding, Roberto Corizzo, Colin Bellinger, Nancy Ching, Spencer Login, Rodrigo Yepez-Lopez, Jie Gong, Dong L. Wu

Proceedings of the IEEE International Conference on Big Data, 2022

Balancing Information with Observation Costs in Deep Reinforcement Learning.

Colin Bellinger, Andriy Drozdyuk, Mark Crowley, Isaac Tamblyn

Proceedings of the 35th Canadian Conference on Artificial Intelligence, Toronto, Ontario, 2022

2021

RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification.

Michal Koziarski, Colin Bellinger, Michal Wozniak

Mach. Learn., 2021

Scientific Discovery and the Cost of Measurement - Balancing Information and Cost in Reinforcement Learning.

Colin Bellinger, Andriy Drozdyuk, Mark Crowley, Isaac Tamblyn

CoRR, 2021

Undersampling with Support Vectors for Multi-Class Imbalanced Data Classification.

Bartosz Krawczyk, Colin Bellinger, Roberto Corizzo, Nathalie Japkowicz

Proceedings of the International Joint Conference on Neural Networks, 2021

Calibrated Resampling for Imbalanced and Long-Tails in Deep Learning.

Colin Bellinger, Roberto Corizzo, Nathalie Japkowicz

Proceedings of the Discovery Science - 24th International Conference, 2021

On the combined effect of class imbalance and concept complexity in deep learning.

Kushankur Ghosh, Colin Bellinger, Roberto Corizzo, Bartosz Krawczyk, Nathalie Japkowicz

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

Explainable image analysis for decision support in medical healthcare.

Roberto Corizzo, Yohan Dauphin, Colin Bellinger, Eftim Zdravevski, Nathalie Japkowicz

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

Active Measure Reinforcement Learning for Observation Cost Minimization.

Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn

Proceedings of the 34th Canadian Conference on Artificial Intelligence, 2021

2020

Framework for extreme imbalance classification: SWIM - sampling with the majority class.

Colin Bellinger, Shiven Sharma, Nathalie Japkowicz, Osmar R. Zaïane

Knowl. Inf. Syst., 2020

ReMix: Calibrated Resampling for Class Imbalance in Deep learning.

Colin Bellinger, Roberto Corizzo, Nathalie Japkowicz

CoRR, 2020

SMOTEFUNA: Synthetic Minority Over-Sampling Technique Based on Furthest Neighbour Algorithm.

Ahmad S. Tarawneh, Ahmad B. A. Hassanat, Khalid Almohammadi, Dmitry Chetverikov, Colin Bellinger

IEEE Access, 2020

Reinforcement Learning in a Physics-Inspired Semi-Markov Environment.

Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn

Proceedings of the Advances in Artificial Intelligence, 2020

2019

The index lift in data mining has a close relationship with the association measure relative risk in epidemiological studies.

Khanh Vu, Rebecca A. Clark, Colin Bellinger, Graham Erickson, Alvaro Osornio-Vargas, Osmar R. Zaïane, Yan Yuan

BMC Medical Informatics Decis. Mak., 2019

The CURE for Class Imbalance.

Colin Bellinger, Paula Branco, Luís Torgo

Proceedings of the Discovery Science - 22nd International Conference, 2019

2018

Manifold-based synthetic oversampling with manifold conformance estimation.

Colin Bellinger, Christopher Drummond, Nathalie Japkowicz

Mach. Learn., 2018

Discovering co-location patterns with aggregated spatial transactions and dependency rules.

Mohomed Shazan Mohomed Jabbar, Colin Bellinger, Osmar R. Zaïane, Alvaro Osornio-Vargas

Int. J. Data Sci. Anal., 2018

One-class classification - From theory to practice: A case-study in radioactive threat detection.

Colin Bellinger, Shiven Sharma, Nathalie Japkowicz

Expert Syst. Appl., 2018

Synthetic Oversampling with the Majority Class: A New Perspective on Handling Extreme Imbalance.

Shiven Sharma, Colin Bellinger, Bartosz Krawczyk, Osmar R. Zaïane, Nathalie Japkowicz

Proceedings of the IEEE International Conference on Data Mining, 2018

2017

Sampling a Longer Life: Binary versus One-class classification Revisited.

Colin Bellinger, Shiven Sharma, Osmar R. Zaïane, Nathalie Japkowicz

Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, 2017

2016

Beyond the Boundaries of SMOTE - A Framework for Manifold-Based Synthetically Oversampling.

Colin Bellinger, Christopher Drummond, Nathalie Japkowicz

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016

Advantage of integration in big data: Feature generation in multi-relational databases for imbalanced learning.

Farrukh Ahmed, Michele Samorani, Colin Bellinger, Osmar R. Zaïane

Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), 2016

2015

Synthetic Oversampling for Advanced Radioactive Threat Detection.

Colin Bellinger, Nathalie Japkowicz, Christopher Drummond

Proceedings of the 14th IEEE International Conference on Machine Learning and Applications, 2015

Multi-label Classification of Anemia Patients.

Colin Bellinger, Ali Amid, Nathalie Japkowicz, Herna L. Viktor

Proceedings of the 14th IEEE International Conference on Machine Learning and Applications, 2015

Active Learning for One-Class Classification.

Vincent Barnabe-Lortie, Colin Bellinger, Nathalie Japkowicz

Proceedings of the 14th IEEE International Conference on Machine Learning and Applications, 2015

2014

Smoothing gamma ray spectra to improve outlier detection.

Vincent Barnabe-Lortie, Colin Bellinger, Nathalie Japkowicz

Proceedings of the Seventh IEEE Symposium on Computational Intelligence for Security and Defense Applications, 2014

2012

On the Pattern Recognition and Classification of Stochastically Episodic Events.

Colin Bellinger, B. John Oommen

Trans. Comput. Collect. Intell., 2012

One-Class versus Binary Classification: Which and When?

Colin Bellinger, Shiven Sharma, Nathalie Japkowicz

Proceedings of the 11th International Conference on Machine Learning and Applications, 2012

Anomaly detection in gamma ray spectra: A machine learning perspective.

Shiven Sharma, Colin Bellinger, Nathalie Japkowicz, Rodney Berg, R. Kurt Ungar

Proceedings of the 2012 IEEE Symposium on Computational Intelligence for Security and Defence Applications, 2012

Clustering Based One-Class Classification for Compliance Verification of the Comprehensive Nuclear-Test-Ban Treaty.

Shiven Sharma, Colin Bellinger, Nathalie Japkowicz

Proceedings of the Advances in Artificial Intelligence, 2012

2011

Motivating the inclusion of meteorological indicators in the CTBT feature-space.

Colin Bellinger, Nathalie Japkowicz

Proceedings of the 2011 IEEE Symposium on Computational Intelligence for Security and Defense Applications, 2011

A New Frontier in Novelty Detection: Pattern Recognition of Stochastically Episodic Events.

Colin Bellinger, B. John Oommen

Proceedings of the Intelligent Information and Database Systems, 2011

2010

On simulating episodic events against a background of noise-like non-episodic events.

Colin Bellinger, B. John Oommen

Proceedings of the SummerSim '10, 2010