Empirical comparisons of various discretizationprocedures

The genuine symbolic machine learning (ML) algorithms are capable of processing symbolic, categorial data only. However, real-world problems, e.g. in medicine or nance, involve both symbolic and numerical attributes. Therefore, there is an important issue of ML to discretize (categorize) numerical attributes. There exist quite a few discretization procedures in the ML eld. This paper describes two newer algorithms for categorization (discretization) of numerical attributes. The rst one is implemented in the KEX (Knowledge EXplorer) as its preprocessing procedure. Its idea is to discretize the numerical attributes in such a way that the resulting categorization ts the way how KEX creates a knowledge base. Nevertheless, the resulting categorization is suitable also for other machine learning algorithms. The other discretization procedure is implemented in CN4, a large extension of the well-known CN2 machine learning algorithm. The range of numerical attributes is devided into intervals that may form a complex generated by the algorithm as a part fo the class description. Experimental results show a comparison of performance of KEX and CN4 on some well-known ML databases. To make the comparison more exhibitory, other ML algorithms such as ID3 and C4.5 were run under our experiments, too. Then, the results are compared and discussed.