Argue to Learn: Accelerated Argumentation-Based Learning

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


Human agents can acquire knowledge and learn through argumentation. Inspired by this fact, we propose a novel argumentation-based machine learning technique that can be used for online incremental learning scenarios. Existing methods for online incremental learning problems typically do not generalize well from just a few learning instances. Our previous argumentation-based online incremental learning method outperformed state-of-the-art methods in terms of accuracy and learning speed. However, it was neither memory-efficient nor computationally efficient since the algorithm used the power set of the feature values for updating the model. In this paper, we propose an accelerated version of the algorithm, with polynomial instead of exponential complexity, while achieving higher learning accuracy. The proposed method is at least 200 times faster than the original argumentation-based learning method and is more memory-efficient.
Original languageEnglish
Title of host publication20th IEEE International Conference on Machine Learning and Applications
Number of pages6
ISBN (Electronic)978-1-6654-4337-1
ISBN (Print)978-1-6654-4338-8
Publication statusPublished - 2021
EventIEEE International Conference on Machine Learning and Applications - Pasadena, California, USA, Pasadena, United States
Duration: 13-Dec-202116-Dec-2021
Conference number: 20


ConferenceIEEE International Conference on Machine Learning and Applications
Abbreviated titleIEEE ICMLA
Country/TerritoryUnited States
Internet address


  • Argumentation-Based Learning
  • Online Incremental Learning
  • Argumentation Theory

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