Framework for stream learning algorithms
International Journal of Computational Intelligence Studies
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Traditionally the algorithm of ID3 is a greedy algorithm which searches and compares the attribute of each level in the decision tree. During the process of selection of expanded attributes, attributes with more values are usually preferred to be selected. It results in a decision tree with large scale consequently, so a new merging branches algorithm EPMID in decision tree is proposed in this paper. The algorithm uses the pre-pruning strategy, and merges the non-leaf branches which have the equal predictability. The experimental results show that the improved algorithm reduced the width and the leaf nodes of the decision tree. The time complexity and space complexity and classification precision are superior to ID3.