Learning weighted fuzzy rules from examples with mixed attributes by fuzzy decision trees

Most fuzzy learning algorithms can generate simple fuzzy rules from a set of examples having the same type of attributes. However, the case for fuzzy rules in which their knowledge representation power has been enhanced by the inclusion of several parameters such as weight and certainty factor has not been addressed. It is important but difficult to automatically acquire fuzzy rules with parameters from a set of examples with mixed attributes. The paper presents an approach to handle mixed attributes and introduces the concept of degree of importance of each attribute-value contributing to the consequent of a given rule. Based on this new concept, a new heuristic for generating fuzzy decision trees with parameters is given and a set of weighted fuzzy rules with local weight and certainty factor are extracted from the trees. The advantages of the learning is initially verified by the Iris classification problem