EVALUATION OF AUTOMATIC RULE INDUCTION SYSTEMS

The effects of number of attributes for description of data set, number of examples included in the training set, and post-pruning mechanism, on the predictability power of the classification rules for automatic assignment of river water pollution levels were studied. In the induction experiments, the original ID3 algorithm embedded in the Knowledge Maker environment was modified by post-pruning mechanism. In order to facilitate the evaluation of the developed classification rules, the algorithm of Reingold and Tilford for tidier drawing of trees was implemented. The results showed that efficient classification rules in comparison with experts' class assignment can already be derived from 500 examples of baseline data, each example being described by 5 attributes.