B. Zupan et S. Dzeroski, ACQUIRING BACKGROUND KNOWLEDGE FOR MACHINE LEARNING USING FUNCTION DECOMPOSITION - A CASE-STUDY IN RHEUMATOLOGY, Artificial intelligence in medicine, 14(1-2), 1998, pp. 101-117
Domain or background knowledge is often needed in order to solve diffi
cult problems of learning medical diagnostic rules. Earlier experiment
s have demonstrated the utility of background knowledge when learning
rules for early diagnosis of rheumatic diseases. A particular form of
background knowledge comprising typical co-occurrences of several grou
ps of attributes was provided by a medical expert. This paper explores
the possibility of automating the process of acquiring background kno
wledge of this kind and studies the utility of such methods in the pro
blem domain of rheumatic diseases. A method based on function decompos
ition is proposed that identifies typical co-occurrences for a given s
et of attributes. The method is evaluated by comparing the typical co;
occurrences it identifies as well as their contribution to the perform
ance of machine learning algorithms, to the ones provided by a medical
expert. (C) 1998 Elsevier Science B.V. All rights reserved.