Improvement of ID3 algorithm based on simplified information entropy and coordination degree

In data classification mining, the decision tree method is a key algorithm. ID3 (Iterative Dichotomiser 3) algorithm which was presented by Quinlan is a famous decision tree algorithms, but ID3 has some shortcomings such as high complex computation in computing the information entropy expression, multivalue bios problem in the process of selecting an optimal attribute, large scales, etc. In order to solve the above problems, the improved ID3 algorithm is proposed, which combines the simplified information entropy with coordination degree in rough set theory. The experiment result has proved the feasibility of the optimized way.

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