The ID3 algorithm, used in machine learning, determines the best way to split data for decision trees. However, it often favors attributes with multiple values, which may not be useful for classification. To address this issue, a new method called revised information gain has been developed. This method improves the ID3 algorithm's ability to handle multi-value attributes, making it more effective in real-world classification tasks.