Colin Bellinger

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 45

Top Topics

Class Imbalance

Imbalanced Data

Reinforcement Learning

Top Venues

Publications

Damien Dablain, Colin Bellinger, Bartosz Krawczyk, David W. Aha, Nitesh V. Chawla
Understanding imbalanced data: XAI & interpretable ML framework. Mach. Learn. 113 (6) (2024)
Kushankur Ghosh, Colin Bellinger, Roberto Corizzo, Paula Branco, Bartosz Krawczyk, Nathalie Japkowicz
The class imbalance problem in deep learning. Mach. Learn. 113 (7) (2024)
Maicon Pierre Lourenço, Jirí Hostas, Colin Bellinger, Alain Tchagang, Dennis R. Salahub
Reinforcement learning for in silico determination of adsorbate - substrate structures. J. Comput. Chem. 45 (15) (2024)
Damien Dablain, Kristen N. Jacobson, Colin Bellinger, Mark Roberts, Nitesh V. Chawla
Understanding CNN fragility when learning with imbalanced data. Mach. Learn. 113 (7) (2024)
Colin Bellinger, Roberto Corizzo, Nathalie Japkowicz
Performance Estimation bias in Class Imbalance with Minority Subconcepts. LIDTA (2023)
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
ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry. CoRR (2023)
Vítor Cerqueira, Luís Torgo, Paula Branco, Colin Bellinger
Automated imbalanced classification via layered learning. Mach. Learn. 112 (6) (2023)
Jonatan Møller Nuutinen Gøttcke, Colin Bellinger, Paula Branco, Arthur Zimek
An Interpretable Measure of Dataset Complexity for Imbalanced Classification Problems. SDM (2023)
Vincent Létourneau, Colin Bellinger, Isaac Tamblyn, Maia Fraser
Time and temporal abstraction in continual learning: tradeoffs, analogies and regret in an active measuring setting. CoLLAs (2023)
Damien A. Dablain, Colin Bellinger, Bartosz Krawczyk, Nitesh V. Chawla
Efficient Augmentation for Imbalanced Deep Learning. ICDE (2023)
Payam Parvizi, Runnan Zou, Colin Bellinger, Ross Cheriton, Davide Spinello
Reinforcement Learning-based Wavefront Sensorless Adaptive Optics Approaches for Satellite-to-Ground Laser Communication. CoRR (2023)
Colin Bellinger, Laurence Lamarche-Cliche
Learning Visual Tracking and Reaching with Deep Reinforcement Learning on a UR10e Robotic Arm. CoRR (2023)
Colin Bellinger, Mark Crowley, Isaac Tamblyn
Learning when to observe: A frugal reinforcement learning framework for a high-cost world. CoRR (2023)
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
4th Workshop on Deep Learning Practice and Theory for High-Dimensional Sparse and Imbalanced Data with KDD 2022. KDD (2022)
Colin Bellinger, Andriy Drozdyuk, Mark Crowley, Isaac Tamblyn
Balancing Information with Observation Costs in Deep Reinforcement Learning. AI (2022)
Leah Ding, Roberto Corizzo, Colin Bellinger, Nancy Ching, Spencer Login, Rodrigo Yepez-Lopez, Jie Gong, Dong L. Wu
Imbalanced Multi-layer Cloud Classification with Advanced Baseline Imager (ABI) and CloudSat/CALIPSO Data. Big Data (2022)
Damien Dablain, Colin Bellinger, Bartosz Krawczyk, Nitesh V. Chawla
Efficient Augmentation for Imbalanced Deep Learning. CoRR (2022)
Damien Dablain, Kristen N. Jacobson, Colin Bellinger, Mark Roberts, Nitesh V. Chawla
Understanding CNN Fragility When Learning With Imbalanced Data. CoRR (2022)
Damien A. Dablain, Colin Bellinger, Bartosz Krawczyk, David W. Aha, Nitesh V. Chawla
Interpretable ML for Imbalanced Data. CoRR (2022)
Vítor Cerqueira, Luís Torgo, Paula Branco, Colin Bellinger
Automated Imbalanced Classification via Layered Learning. CoRR (2022)
Michal Koziarski, Colin Bellinger, Michal Wozniak
RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification. DSAA (2021)
Bartosz Krawczyk, Colin Bellinger, Roberto Corizzo, Nathalie Japkowicz
Undersampling with Support Vectors for Multi-Class Imbalanced Data Classification. IJCNN (2021)
Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn
Active Measure Reinforcement Learning for Observation Cost Minimization. Canadian Conference on AI (2021)
Colin Bellinger, Andriy Drozdyuk, Mark Crowley, Isaac Tamblyn
Scientific Discovery and the Cost of Measurement - Balancing Information and Cost in Reinforcement Learning. CoRR (2021)
Roberto Corizzo, Yohan Dauphin, Colin Bellinger, Eftim Zdravevski, Nathalie Japkowicz
Explainable image analysis for decision support in medical healthcare. IEEE BigData (2021)
Colin Bellinger, Roberto Corizzo, Nathalie Japkowicz
Calibrated Resampling for Imbalanced and Long-Tails in Deep Learning. DS (2021)
Kushankur Ghosh, Colin Bellinger, Roberto Corizzo, Bartosz Krawczyk, Nathalie Japkowicz
On the combined effect of class imbalance and concept complexity in deep learning. IEEE BigData (2021)
Michal Koziarski, Colin Bellinger, Michal Wozniak
RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification. Mach. Learn. 110 (11) (2021)
Michal Koziarski, Colin Bellinger, Michal Wozniak
RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification. CoRR (2021)
Kushankur Ghosh, Colin Bellinger, Roberto Corizzo, Bartosz Krawczyk, Nathalie Japkowicz
On the combined effect of class imbalance and concept complexity in deep learning. CoRR (2021)
Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn
Reinforcement Learning in a Physics-Inspired Semi-Markov Environment. Canadian Conference on AI (2020)
Ahmad S. Tarawneh, Ahmad B. A. Hassanat, Khalid Almohammadi, Dmitry Chetverikov, Colin Bellinger
SMOTEFUNA: Synthetic Minority Over-Sampling Technique Based on Furthest Neighbour Algorithm. IEEE Access 8 (2020)
Colin Bellinger, Roberto Corizzo, Nathalie Japkowicz
ReMix: Calibrated Resampling for Class Imbalance in Deep learning. CoRR (2020)
Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn
Active Measure Reinforcement Learning for Observation Cost Minimization. CoRR (2020)
Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn
Reinforcement Learning in a Physics-Inspired Semi-Markov Environment. CoRR (2020)
Colin Bellinger, Shiven Sharma, Nathalie Japkowicz, Osmar R. Zaïane
Framework for extreme imbalance classification: SWIM - sampling with the majority class. Knowl. Inf. Syst. 62 (3) (2020)
Khanh Vu, Rebecca A. Clark, Colin Bellinger, Graham Erickson, Alvaro Osornio-Vargas, Osmar R. Zaïane, Yan Yuan
The index lift in data mining has a close relationship with the association measure relative risk in epidemiological studies. BMC Medical Informatics Decis. Mak. 19 (1) (2019)
Colin Bellinger, Paula Branco, Luís Torgo
The CURE for Class Imbalance. DS (2019)
Colin Bellinger, Christopher Drummond, Nathalie Japkowicz
Manifold-based synthetic oversampling with manifold conformance estimation. Mach. Learn. 107 (3) (2018)
Shiven Sharma, Colin Bellinger, Bartosz Krawczyk, Osmar R. Zaïane, Nathalie Japkowicz
Synthetic Oversampling with the Majority Class: A New Perspective on Handling Extreme Imbalance. ICDM (2018)
Mohomed Shazan Mohomed Jabbar, Colin Bellinger, Osmar R. Zaïane, Alvaro Osornio-Vargas
Discovering co-location patterns with aggregated spatial transactions and dependency rules. Int. J. Data Sci. Anal. 5 (2-3) (2018)
Colin Bellinger, Shiven Sharma, Nathalie Japkowicz
One-class classification - From theory to practice: A case-study in radioactive threat detection. Expert Syst. Appl. 108 (2018)
Colin Bellinger, Shiven Sharma, Osmar R. Zaïane, Nathalie Japkowicz
Sampling a Longer Life: Binary versus One-class classification Revisited. LIDTA@PKDD/ECML (2017)
Colin Bellinger, Christopher Drummond, Nathalie Japkowicz
Beyond the Boundaries of SMOTE - A Framework for Manifold-Based Synthetically Oversampling. ECML/PKDD (1) (2016)
Farrukh Ahmed, Michele Samorani, Colin Bellinger, Osmar R. Zaïane
Advantage of integration in big data: Feature generation in multi-relational databases for imbalanced learning. IEEE BigData (2016)
Colin Bellinger, Ali Amid, Nathalie Japkowicz, Herna L. Viktor
Multi-label Classification of Anemia Patients. ICMLA (2015)
Colin Bellinger, Nathalie Japkowicz, Christopher Drummond
Synthetic Oversampling for Advanced Radioactive Threat Detection. ICMLA (2015)
Vincent Barnabe-Lortie, Colin Bellinger, Nathalie Japkowicz
Active Learning for One-Class Classification. ICMLA (2015)
Vincent Barnabe-Lortie, Colin Bellinger, Nathalie Japkowicz
Smoothing gamma ray spectra to improve outlier detection. CISDA (2014)
Colin Bellinger, Shiven Sharma, Nathalie Japkowicz
One-Class versus Binary Classification: Which and When? ICMLA (2) (2012)
Shiven Sharma, Colin Bellinger, Nathalie Japkowicz
Clustering Based One-Class Classification for Compliance Verification of the Comprehensive Nuclear-Test-Ban Treaty. Canadian Conference on AI (2012)
Colin Bellinger, B. John Oommen
On the Pattern Recognition and Classification of Stochastically Episodic Events. Trans. Comput. Collect. Intell. 6 (2012)
Shiven Sharma, Colin Bellinger, Nathalie Japkowicz, Rodney Berg, R. Kurt Ungar
Anomaly detection in gamma ray spectra: A machine learning perspective. CISDA (2012)
Colin Bellinger, Nathalie Japkowicz
Motivating the inclusion of meteorological indicators in the CTBT feature-space. CISDA (2011)
Colin Bellinger, B. John Oommen
A New Frontier in Novelty Detection: Pattern Recognition of Stochastically Episodic Events. ACIIDS (1) (2011)
Colin Bellinger, B. John Oommen
On simulating episodic events against a background of noise-like non-episodic events. SummerSim (2010)