Continuous Classes in Rule Induction: Empirical Comparison of Two Approaches

. This paper introduces modification of two existing rule-inducing ML algorithms; they are now capable of processing continuous classes. The first one is the CN4 algorithm, a large extension of the well-known CN2. It invokes its discretization procedure and also deals with continuous classes within the inductive process itself, i.e., 'on-line'. The other ML system, KEX (Knowledge EXplorer), treats numerical attributes by its preprocessor, that is, they are discretized 'off-line'; the continuous classes are processed within the inductive algorithm itself. Experimental results show a comparison of KEX and CN4 on some realworld as well as artificial data. 1 Introduction The genuine symbolic machine learning (ML) algorithms were designed to process symbolic data accompanied by symbolic classes (concepts) only. However, real-world data, e.g., in medicine or financing, consist of both symbolic and numerical data (attributes) and some applications even exhibit continuous classes (concepts). ..