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2020 – today
- 2024
[j13]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): 1289-1302 (2024)- [j12]Damien Dablain, Colin Bellinger, Bartosz Krawczyk, David W. Aha, Nitesh V. Chawla:
Understanding imbalanced data: XAI & interpretable ML framework. Mach. Learn. 113(6): 3751-3769 (2024) - [j11]Damien Dablain, Kristen N. Jacobson, Colin Bellinger, Mark Roberts, Nitesh V. Chawla:
Understanding CNN fragility when learning with imbalanced data. Mach. Learn. 113(7): 4785-4810 (2024) - [j10]Kushankur Ghosh, Colin Bellinger, Roberto Corizzo, Paula Branco, Bartosz Krawczyk, Nathalie Japkowicz:
The class imbalance problem in deep learning. Mach. Learn. 113(7): 4845-4901 (2024) - [c32]Gautham Vasan, Mohamed Elsayed, Seyed Alireza Azimi, Jiamin He, Fahim Shahriar, Colin Bellinger, Martha White, Rupam Mahmood:
Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers. NeurIPS 2024 - [c31]Ethan Fettes, Pablo G. Madoery, Halim Yanikomeroglu, Gunes Karabulut Kurt, Colin Bellinger, Stéphane Martel, Khaled Ahmed, Sameera Siddiqui:
Next-Generation Satellite IoT Networks: A HAPS-Enabled Solution to Enhance Optical Data Transfer. PIMRC 2024: 1-6 - [i16]Ethan Fettes, Pablo G. Madoery, Halim Yanikomeroglu, Gunes Karabulut Kurt, Colin Bellinger, Stéphane Martel, Khaled Ahmed, Sameera Siddiqui:
Next-Generation Satellite IoT Networks: A HAPS-Enabled Solution to Enhance Optical Data Transfer. CoRR abs/2408.09281 (2024) - [i15]Gautham Vasan, Mohamed Elsayed, Alireza Azimi, Jiamin He, Fahim Shahriar, Colin Bellinger, Martha White, A. Rupam Mahmood:
Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers. CoRR abs/2411.15370 (2024) - 2023
- [j9]Vítor Cerqueira, Luís Torgo, Paula Branco, Colin Bellinger:
Automated imbalanced classification via layered learning. Mach. Learn. 112(6): 2083-2104 (2023) - [c30]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: 470-480 - [c29]Damien A. Dablain, Colin Bellinger, Bartosz Krawczyk, Nitesh V. Chawla:
Efficient Augmentation for Imbalanced Deep Learning. ICDE 2023: 1433-1446 - [c28]Colin Bellinger, Roberto Corizzo, Nathalie Japkowicz:
Performance Estimation bias in Class Imbalance with Minority Subconcepts. LIDTA 2023: 31-44 - [c27]Colin Bellinger, Isaac Tamblyn, Mark Crowley:
Learning When to Observe: A Frugal Reinforcement Learning Framework for a High-Cost World. PKDD/ECML Workshops (4) 2023: 242-257 - [c26]Jonatan Møller Nuutinen Gøttcke, Colin Bellinger, Paula Branco, Arthur Zimek:
An Interpretable Measure of Dataset Complexity for Imbalanced Classification Problems. SDM 2023: 253-261 - [i14]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 abs/2303.07516 (2023) - [i13]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 abs/2305.14177 (2023) - [i12]Colin Bellinger, Mark Crowley, Isaac Tamblyn:
Learning when to observe: A frugal reinforcement learning framework for a high-cost world. CoRR abs/2307.02620 (2023) - [i11]Colin Bellinger, Laurence Lamarche-Cliche:
Learning Visual Tracking and Reaching with Deep Reinforcement Learning on a UR10e Robotic Arm. CoRR abs/2308.14652 (2023) - 2022
- [c25]Colin Bellinger, Andriy Drozdyuk, Mark Crowley, Isaac Tamblyn:
Balancing Information with Observation Costs in Deep Reinforcement Learning. Canadian AI 2022 - [c24]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. IEEE Big Data 2022: 5902-5909 - [c23]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: 4860-4861 - [i10]Vítor Cerqueira, Luís Torgo, Paula Branco, Colin Bellinger:
Automated Imbalanced Classification via Layered Learning. CoRR abs/2205.02553 (2022) - [i9]Damien Dablain, Colin Bellinger, Bartosz Krawczyk, Nitesh V. Chawla:
Efficient Augmentation for Imbalanced Deep Learning. CoRR abs/2207.06080 (2022) - [i8]Damien Dablain, Kristen N. Jacobson, Colin Bellinger, Mark Roberts, Nitesh V. Chawla:
Understanding CNN Fragility When Learning With Imbalanced Data. CoRR abs/2210.09465 (2022) - [i7]Damien A. Dablain, Colin Bellinger, Bartosz Krawczyk, David W. Aha, Nitesh V. Chawla:
Interpretable ML for Imbalanced Data. CoRR abs/2212.07743 (2022) - 2021
- [j8]Michal Koziarski, Colin Bellinger, Michal Wozniak:
RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification. Mach. Learn. 110(11): 3059-3093 (2021) - [c22]Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn:
Active Measure Reinforcement Learning for Observation Cost Minimization. Canadian AI 2021 - [c21]Roberto Corizzo, Yohan Dauphin, Colin Bellinger, Eftim Zdravevski, Nathalie Japkowicz:
Explainable image analysis for decision support in medical healthcare. IEEE BigData 2021: 4667-4674 - [c20]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: 4859-4868 - [c19]Colin Bellinger, Roberto Corizzo, Nathalie Japkowicz:
Calibrated Resampling for Imbalanced and Long-Tails in Deep Learning. DS 2021: 242-252 - [c18]Michal Koziarski, Colin Bellinger, Michal Wozniak:
RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification. DSAA 2021: 1-2 - [c17]Bartosz Krawczyk, Colin Bellinger, Roberto Corizzo, Nathalie Japkowicz:
Undersampling with Support Vectors for Multi-Class Imbalanced Data Classification. IJCNN 2021: 1-7 - [i6]Michal Koziarski, Colin Bellinger, Michal Wozniak:
RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification. CoRR abs/2105.04009 (2021) - [i5]Kushankur Ghosh, Colin Bellinger, Roberto Corizzo, Bartosz Krawczyk, Nathalie Japkowicz:
On the combined effect of class imbalance and concept complexity in deep learning. CoRR abs/2107.14194 (2021) - [i4]Colin Bellinger, Andriy Drozdyuk, Mark Crowley, Isaac Tamblyn:
Scientific Discovery and the Cost of Measurement - Balancing Information and Cost in Reinforcement Learning. CoRR abs/2112.07535 (2021) - 2020
- [j7]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: 59069-59082 (2020) - [j6]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): 841-866 (2020) - [c16]Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn:
Reinforcement Learning in a Physics-Inspired Semi-Markov Environment. Canadian AI 2020: 55-66 - [i3]Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn:
Reinforcement Learning in a Physics-Inspired Semi-Markov Environment. CoRR abs/2004.07333 (2020) - [i2]Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn:
Active Measure Reinforcement Learning for Observation Cost Minimization. CoRR abs/2005.12697 (2020) - [i1]Colin Bellinger, Roberto Corizzo, Nathalie Japkowicz:
ReMix: Calibrated Resampling for Class Imbalance in Deep learning. CoRR abs/2012.02312 (2020)
2010 – 2019
- 2019
- [j5]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): 112:1-112:8 (2019) - [c15]Colin Bellinger, Paula Branco, Luís Torgo:
The CURE for Class Imbalance. DS 2019: 3-17 - 2018
- [j4]Colin Bellinger, Shiven Sharma, Nathalie Japkowicz:
One-class classification - From theory to practice: A case-study in radioactive threat detection. Expert Syst. Appl. 108: 223-232 (2018) - [j3]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): 137-154 (2018) - [j2]Colin Bellinger, Christopher Drummond, Nathalie Japkowicz:
Manifold-based synthetic oversampling with manifold conformance estimation. Mach. Learn. 107(3): 605-637 (2018) - [c14]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: 447-456 - 2017
- [c13]Colin Bellinger, Shiven Sharma, Osmar R. Zaïane, Nathalie Japkowicz:
Sampling a Longer Life: Binary versus One-class classification Revisited. LIDTA@PKDD/ECML 2017: 64-78 - 2016
- [c12]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: 532-539 - [c11]Colin Bellinger, Christopher Drummond, Nathalie Japkowicz:
Beyond the Boundaries of SMOTE - A Framework for Manifold-Based Synthetically Oversampling. ECML/PKDD (1) 2016: 248-263 - 2015
- [c10]Vincent Barnabe-Lortie, Colin Bellinger, Nathalie Japkowicz:
Active Learning for One-Class Classification. ICMLA 2015: 390-395 - [c9]Colin Bellinger, Ali Amid, Nathalie Japkowicz, Herna L. Viktor:
Multi-label Classification of Anemia Patients. ICMLA 2015: 825-830 - [c8]Colin Bellinger, Nathalie Japkowicz, Christopher Drummond:
Synthetic Oversampling for Advanced Radioactive Threat Detection. ICMLA 2015: 948-953 - 2014
- [c7]Vincent Barnabe-Lortie, Colin Bellinger, Nathalie Japkowicz:
Smoothing gamma ray spectra to improve outlier detection. CISDA 2014: 1-8 - 2012
- [j1]Colin Bellinger, B. John Oommen:
On the Pattern Recognition and Classification of Stochastically Episodic Events. Trans. Comput. Collect. Intell. 6: 1-35 (2012) - [c6]Shiven Sharma, Colin Bellinger, Nathalie Japkowicz:
Clustering Based One-Class Classification for Compliance Verification of the Comprehensive Nuclear-Test-Ban Treaty. Canadian AI 2012: 181-193 - [c5]Shiven Sharma, Colin Bellinger, Nathalie Japkowicz, Rodney Berg, R. Kurt Ungar:
Anomaly detection in gamma ray spectra: A machine learning perspective. CISDA 2012: 1-8 - [c4]Colin Bellinger, Shiven Sharma, Nathalie Japkowicz:
One-Class versus Binary Classification: Which and When? ICMLA (2) 2012: 102-106 - 2011
- [c3]Colin Bellinger, B. John Oommen:
A New Frontier in Novelty Detection: Pattern Recognition of Stochastically Episodic Events. ACIIDS (1) 2011: 435-444 - [c2]Colin Bellinger, Nathalie Japkowicz:
Motivating the inclusion of meteorological indicators in the CTBT feature-space. CISDA 2011: 88-95 - 2010
- [c1]Colin Bellinger, B. John Oommen:
On simulating episodic events against a background of noise-like non-episodic events. SummerSim 2010: 452-460
Coauthor Index
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