Is it acceptable to select a random child node when using a Decision Tree (trained via ID3) to predict if an unknown attribute value is encountered
Do your leaf nodes return a probability distribution rather than a single class value e.g., the majority class of training instances that arrived at the node? If so, a better approach would be to send the test instance down every child path and average the probability distributions that are returned by the leaves. 

Your good score of 95.45% on iris data suggests to me that it's not a very important detail in this example. Try some more challenging datasets and see if it makes a difference.