DescriptionStandard machine learning takes examples as input in the form of pairs (A, C), where A is an attribute value vector and C the class the example belongs to e.g., (Name=Mrs.Brown, PaysRegularly=no, Rich=yes, HairColor=Blond)^T, C=(CreditApproved=Yes).
In contrast, argument-based machine learning (ABML) is able to process examples of the form (A, C, Arguments), where Arguments is a set of type
- C because of reasons, or
- C despite reasons.
- A = (Name=Mrs.Brown, PaysRegularly=no, Rich=yes, HairColor=Blond)^T
- C = (CreditApproved=Yes)
- Arguments = (C because of Rich, C despite PaysRegularly = No)
- Lucas Carstens, 2011, Sentiment Analysis - A multimodal approach, Master Thesis, Imperial College, London
- MoÅ¾ina, M. et al., 2008. Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning. In Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence. Amsterdam, The Netherlands, The Netherlands: IOS Press, pp. 234—238.
- MoÅ¾ina, M. - Argument-based Machine Learning - http://www.ailab.si/martin/abml