Argument based machine learning in a medical domain

Authors:
Jure Žabkar;Martin Možina;Jerneja Videčnik;Ivan Bratko
Affiliations:
Faculty of Computer and Information Science, University of Ljubljana, Slovenia;Faculty of Computer and Information Science, University of Ljubljana, Slovenia;Clinic for Infectious Diseases, Ljubljana, Slovenia;Faculty of Computer and Information Science, University of Ljubljana, Slovenia
Venue:
Proceedings of the 2006 conference on Computational Models of Argument: Proceedings of COMMA 2006
Year:
2006

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Abstract

Argument Based Machine Learning (ABML) is a new approach to machine learning in which the learning examples can be accompanied by arguments. The arguments for specific examples are a special form of expert's knowledge which the expert uses to substantiate the class value for the chosen example. Možina et al. developed the ABCN2 algorithm-an extension of the well known rule learning algorithm CN2-that can use argumented examples in the learning process. In this work we present an application of ABCN2 in the medical domain which deals with severe bacterial infections in geriatric population. The elderly population, people over 65 years of age, is rapidly growing as well as the costs of treating this population. In our study, we compare ABCN2 to CN2 and show that using arguments we improve the characteristics of the model. We also report the results that C4.5, Naïve Bayes and Logistic Regression achieve in this domain.