The decision tree algorithm is a core technology in data classification mining, and ID3\n(Iterative Dichotomiser 3) algorithm is a famous one, which has achieved good results in the field\nof classification mining. Nevertheless, there exist some disadvantages of ID3 such as attributes biasing\nmulti-values, high complexity, large scales, etc. In this paper, an improved ID3 algorithm is proposed\nthat combines the simplified information entropy based on different weights with coordination degree\nin rough set theory. The traditional ID3 algorithm and the proposed one are fairly compared by using\nthree common data samples as well as the decision tree classifiers. It is shown that the proposed\nalgorithm has a better performance in the running time and tree structure, but not in accuracy than\nthe ID3 algorithm, for the first two sample sets, which are small. For the third sample set that is large,\nthe proposed algorithm improves the ID3 algorithm for all of the running time, tree structure and\naccuracy. The experimental results show that the proposed algorithm is effective and viable.
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