Improving decision tree performance by exception handling

This paper focuses on improving decision tree induction algorithms when a kind of tie appears during the rule generation procedure for specific training datasets. The tie occurs when there are equal proportions of the target class outcome in the leaf node's records that leads to a situation where majority voting cannot be applied. To solve the above mentioned exception, we propose to base the prediction of the result on the naive Bayes(NB)estimate, k-nearest neighbour(k-NN)and association rule mining(ARM). The other features used for splitting the parent nodes are also taken into consideration.