ABML knowledge refinement loop: a case study

Authors:
Matej Guid;Martin Možina;Vida Groznik;Dejan Georgiev;Aleksander Sadikov;Zvezdan Pirtošek;Ivan Bratko
Affiliations:
Faculty of Computer and Information Science, University of Ljubljana, Slovenia;Faculty of Computer and Information Science, University of Ljubljana, Slovenia;Faculty of Computer and Information Science, University of Ljubljana, Slovenia;Department of Neurology, University Medical Centre Ljubljana, Slovenia;Faculty of Computer and Information Science, University of Ljubljana, Slovenia;Department of Neurology, University Medical Centre Ljubljana, Slovenia;Faculty of Computer and Information Science, University of Ljubljana, Slovenia
Venue:
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
Year:
2012

Citing 7
Cited 0

Machine learning: applications in expert systems and information retrieval

Machine learning: applications in expert systems and information retrieval
Applications of machine learning and rule induction

Communications of the ACM
An Experimental Evaluation of Integrating Machine Learning with Knowledge Acquisition

Machine Learning
Argument based machine learning

Artificial Intelligence
Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning

Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Building comprehensible customer churn prediction models with advanced rule induction techniques

Expert Systems with Applications: An International Journal
Elicitation of neurological knowledge with ABML

AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Argument Based Machine Learning (ABML) was recently demonstrated to offer significant benefits for knowledge elicitation. In knowledge acquisition, ABML is used by a domain expert in the so-called ABML knowledge refinement loop. This draws the expert's attention to the most critical parts of the current knowledge base, and helps the expert to argue about critical concrete cases in terms of the expert's own understanding of such cases. Knowledge elicited through ABML refinement loop is therefore more consistent with expert's knowledge and thus leads to more comprehensible models in comparison with other ways of knowledge acquisition with machine learning from examples. Whereas the ABML learning method has been described elsewhere, in this paper we concentrate on detailed mechanisms of the ABML knowledge refinement loop. We illustrate these mechanisms with examples from a case study in the acquisition of neurological knowledge, and provide quantitative results that demonstrate how the model evolving through the ABML loop becomes increasingly more consistent with the expert's knowledge during the process.