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Choice of a knowledge representation method for learning classifiers in medical domains

Śnieżyński B.

Journal of Medical Informatics & Technologies

2005

Vol. 9

195--200

Knowledge based systems in medical domains are common nowadays. Machine learning techniques are broadly used to generate knowledge for such systems. Developers have to choose not only the learning method, but also, what is even more important, the knowledge representation method. The most common criterion for such a choice is prediction accuracy. In the paper we argue that in certain cases knowledge representation, and its simplicity and intelligibility, are more important. In this paper results of experiments performed using several medical data sets and chosen machine learning algorithms are presented. Next, some examples of learned classifiers are shown. Analysis of results conclude the work.