Improving ID3 Algorithm by Combining Values from Equally Important Attributes

ID3 is a well-known algorithm which is used in the classification task of the decision tree learning. Although a lot of research provides improvements on the traditional ID3 algorithm with various strategies, no attempt was made on ID3 to address the problem when there are more than one attribute that can be placed at a particular node, i.e. those attributes are equally important. This paper proposes a new variation of ID3 to combine equally important attributes into a single node of the decision tree classification. The Connect-4 dataset from UCI is used in our experiment since the dataset contains many attributes and instances which can easily encounter the equally important attribute problem. The experimental results show that our proposed method significantly reduces the average depth of the decision tree generated by ID3 algorithm while the average accuracy rate is still preserved.