Rule Generation Versus Decision Tree Induction

M.M. Oprea (Romania)


machine learning, inductive learning, decision tree induction, rule generation.


The success of an expert system depends mainly on the existance of a complete, coherent and non redundant knowledge base. Knowledge base generation can be made by using inductive learning algorithms. The paper presents a comparative study between different inductive learning algorithms, ID3, C4.5, ILA, DCL and RITIO. The main purpose of the study was to identify which class of inductive learning algorithms has a better behaviour, the decision tree based or those not based on decision tree.

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