Judgemental Minimal and Maximal Rules Learning and Its Application

This paper conducts attribute reduction for training set using rough set theory, and then obtains the decision tree rules by use of decision tree algorithm. Afterwards, two criteria on rules screening are proposed in accordance with the concept of the rule information quantity and rule credibility, and the two criteria are applied to minimal and maximal rules learning method, which forms the judgemental minimal and maximal rule learning. Have the algorithm using to decision tree rules simplification, which can narrow the scope of simplification and ensure consistency of coverage of the rules, and the total number of rules are also be reduced.