Abstract
In this paper an algorithm is proposed for the extraction of rules from decision trees for nominal data that could be even in the form of free text. The goal of the algorithm is to process a decision tree generated as input, using the classification algorithm ID3 (Quinlan, 1986, 1993), and to extract rules expressed in natural language. The input tree in this case is full of complex nominal expressions in the form of free text. The whole system is tested with two examples taken from the field of stock market news and medical knowledge.
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