On simulating episodic events against a background of noise-like non-episodic events. - Colin Bellinger; B. John Oommen (SummerSim, 2010)

A New Frontier in Novelty Detection: Pattern Recognition of Stochastically Episodic Events. - Colin Bellinger; B. John Oommen (ACIIDS (1), 2011)

Motivating the inclusion of meteorological indicators in the CTBT feature-space. - Colin Bellinger; Nathalie Japkowicz (CISDA, 2011)

Clustering Based One-Class Classification for Compliance Verification of the Comprehensive Nuclear-Test-Ban Treaty. - Shiven Sharma; Colin Bellinger; Nathalie Japkowicz (Canadian AI, 2012)

Anomaly detection in gamma ray spectra: A machine learning perspective. - Shiven Sharma; Colin Bellinger; Nathalie Japkowicz; Rodney Berg; R. Kurt Ungar (CISDA, 2012)

One-Class versus Binary Classification: Which and When? - Colin Bellinger; Shiven Sharma; Nathalie Japkowicz (ICMLA (2), 2012)

On the Pattern Recognition and Classification of Stochastically Episodic Events. - Colin Bellinger; B. John Oommen (Trans. Comput. Collect. Intell., 2012)

Smoothing gamma ray spectra to improve outlier detection. - Vincent Barnabe-Lortie; Colin Bellinger; Nathalie Japkowicz (CISDA, 2014)

Active Learning for One-Class Classification. - Vincent Barnabe-Lortie; Colin Bellinger; Nathalie Japkowicz (ICMLA, 2015)

Multi-label Classification of Anemia Patients. - Colin Bellinger; Ali Amid; Nathalie Japkowicz; Herna L. Viktor (ICMLA, 2015)

Synthetic Oversampling for Advanced Radioactive Threat Detection. - Colin Bellinger; Nathalie Japkowicz; Christopher Drummond (ICMLA, 2015)

Advantage of integration in big data: Feature generation in multi-relational databases for imbalanced learning. - Farrukh Ahmed; Michele Samorani; Colin Bellinger; Osmar R. Zaïane (IEEE BigData, 2016)

Beyond the Boundaries of SMOTE - A Framework for Manifold-Based Synthetically Oversampling. - Colin Bellinger; Christopher Drummond; Nathalie Japkowicz (ECML/PKDD (1), 2016)

Sampling a Longer Life: Binary versus One-class classification Revisited. - Colin Bellinger; Shiven Sharma; Osmar R. Zaïane; Nathalie Japkowicz (LIDTA@PKDD/ECML, 2017)

Synthetic Oversampling with the Majority Class: A New Perspective on Handling Extreme Imbalance. - Shiven Sharma; Colin Bellinger; Bartosz Krawczyk; Osmar R. Zaïane; Nathalie Japkowicz (ICDM, 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)

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)

Manifold-based synthetic oversampling with manifold conformance estimation. - Colin Bellinger; Christopher Drummond; Nathalie Japkowicz (Mach. Learn., 2018)

The CURE for Class Imbalance. - Colin Bellinger; Paula Branco; Luís Torgo (DS, 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)

Reinforcement Learning in a Physics-Inspired Semi-Markov Environment. - Colin Bellinger; Rory Coles; Mark Crowley 001; Isaac Tamblyn (Canadian AI, 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)

ReMix: Calibrated Resampling for Class Imbalance in Deep learning. - Colin Bellinger; Roberto Corizzo; Nathalie Japkowicz (CoRR, 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)

Active Measure Reinforcement Learning for Observation Cost Minimization. - Colin Bellinger; Rory Coles; Mark Crowley 001; Isaac Tamblyn (Canadian AI, 2021)

Explainable image analysis for decision support in medical healthcare. - Roberto Corizzo; Yohan Dauphin; Colin Bellinger; Eftim Zdravevski; Nathalie Japkowicz (IEEE BigData, 2021)

On the combined effect of class imbalance and concept complexity in deep learning. - Kushankur Ghosh; Colin Bellinger; Roberto Corizzo; Bartosz Krawczyk; Nathalie Japkowicz (IEEE BigData, 2021)

Calibrated Resampling for Imbalanced and Long-Tails in Deep Learning. - Colin Bellinger; Roberto Corizzo; Nathalie Japkowicz (DS, 2021)

Undersampling with Support Vectors for Multi-Class Imbalanced Data Classification. - Bartosz Krawczyk; Colin Bellinger; Roberto Corizzo; Nathalie Japkowicz (IJCNN, 2021)

Scientific Discovery and the Cost of Measurement - Balancing Information and Cost in Reinforcement Learning. - Colin Bellinger; Andriy Drozdyuk; Mark Crowley 001; Isaac Tamblyn (CoRR, 2021)

RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification. - Michal Koziarski; Colin Bellinger; Michal Wozniak 001 (Mach. Learn., 2021)

Balancing Information with Observation Costs in Deep Reinforcement Learning. - Colin Bellinger; Andriy Drozdyuk; Mark Crowley 001; Isaac Tamblyn (Canadian AI, 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 (IEEE Big Data, 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 001; Biye Jiang; Jiaxin Mao; et al. (KDD, 2022)

Interpretable ML for Imbalanced Data. - Damien A. Dablain; Colin Bellinger; Bartosz Krawczyk; David W. Aha; Nitesh V. Chawla (CoRR, 2022)

Time and temporal abstraction in continual learning: tradeoffs, analogies and regret in an active measuring setting. - Vincent Létourneau; Colin Bellinger; Isaac Tamblyn; Maia Fraser (CoLLAs, 2023)

Efficient Augmentation for Imbalanced Deep Learning. - Damien A. Dablain; Colin Bellinger; Bartosz Krawczyk; Nitesh V. Chawla (ICDE, 2023)

Performance Estimation bias in Class Imbalance with Minority Subconcepts. - Colin Bellinger; Roberto Corizzo; Nathalie Japkowicz (LIDTA, 2023)

Learning When to Observe: A Frugal Reinforcement Learning Framework for a High-Cost World. - Colin Bellinger; Isaac Tamblyn; Mark Crowley 001 (PKDD/ECML Workshops (4), 2023)

An Interpretable Measure of Dataset Complexity for Imbalanced Classification Problems. - Jonatan Møller Nuutinen Gøttcke; Colin Bellinger; Paula Branco; Arthur Zimek (SDM, 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)

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 001; et al. (CoRR, 2023)

Learning Visual Tracking and Reaching with Deep Reinforcement Learning on a UR10e Robotic Arm. - Colin Bellinger; Laurence Lamarche-Cliche (CoRR, 2023)

Automated imbalanced classification via layered learning. - Vítor Cerqueira; Luís Torgo; Paula Branco; Colin Bellinger (Mach. Learn., 2023)

Dynamic programming with incomplete information to overcome navigational uncertainty in POMDPs. - Chris Beeler; Xinkai Li; Colin Bellinger; Mark Crowley 001; Maia Fraser; Isaac Tamblyn (Canadian AI, 2024)

Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers. - Gautham Vasan; Mohamed Elsayed 003; Seyed Alireza Azimi; Jiamin He; Fahim Shahriar; Colin Bellinger; Martha White; Rupam Mahmood (NeurIPS, 2024)

Next-Generation Satellite IoT Networks: A HAPS-Enabled Solution to Enhance Optical Data Transfer. - Ethan Fettes; Pablo G. Madoery; Halim Yanikomeroglu; Gunes Karabulut Kurt; Colin Bellinger; Stéphane Martel; Khaled Ahmed 005; Sameera Siddiqui (PIMRC, 2024)

Reinforcement learning for in silico determination of adsorbate - substrate structures. - Maicon Pierre Lourenço; Jirí Hostas; Colin Bellinger; Alain Tchagang; Dennis R. Salahub (J. Comput. Chem., 2024)

Understanding imbalanced data: XAI & interpretable ML framework. - Damien Dablain; Colin Bellinger; Bartosz Krawczyk; David W. Aha; Nitesh V. Chawla (Mach. Learn., 2024)

Understanding CNN fragility when learning with imbalanced data. - Damien Dablain; Kristen N. Jacobson; Colin Bellinger; Mark Roberts; Nitesh V. Chawla (Mach. Learn., 2024)

The class imbalance problem in deep learning. - Kushankur Ghosh; Colin Bellinger; Roberto Corizzo; Paula Branco; Bartosz Krawczyk; Nathalie Japkowicz (Mach. Learn., 2024)

Energy-Efficient Satellite IoT Optical Downlinks Using Weather-Adaptive Reinforcement Learning. - Ethan Fettes; Pablo G. Madoery; Halim Yanikomeroglu; Gunes Karabulut-Kurt; Abhishek Naik; Colin Bellinger; Stephane Martel; Khaled Ahmed 005; Sameera Siddiqui (CoRR, 2025)

Versatile and Generalizable Manipulation via Goal-Conditioned Reinforcement Learning with Grounded Object Detection. - Huiyi Wang; Fahim Shahriar; Alireza Azimi; Gautham Vasan; Rupam Mahmood; Colin Bellinger (CoRR, 2025)

Dynamic Activation and Assignment of SDN Controllers in LEO Satellite Constellations. - Wafa Hasanain; Pablo G. Madoery; Halim Yanikomeroglu; Gunes Karabulut Kurt; Sameera Siddiqui; Stephane Martel; Khaled Ahmed 005; Colin Bellinger (CoRR, 2025)