Decision Tree Using Artificial Neural Network: A Proposed Model

A decision tree is one of the data mining techniques; it has a tree structure, which consists of internal node, branches, and leaf nodes which are known as decision nodes also. Decision tree works in a transparent way [1]. Unlike artificial neural networks, it shows all the possible alternatives and traces each alternative to its conclusion so that we can easily compare between various alternatives. This is the reason that in spite of being the most powerful technique for pattern recognition, the adoption of ANNs in many areas has been impeded, due to their inability to explain how they came to their conclusion. If large amounts of data are encountered, poor statistical efficiency observed in decision trees and it can be nullified by using neural nets. So, if we could take the knowledge that is acquired by an artificial neural network and express the same knowledge in a decision tree, then explaining a particular decision would be much easier. This paper proposes a model to combine decision tree with artificial neural network. This paper shows how a decision tree will behave and when it will combine with an artificial neural network. The experiment is done on a dataset, and the results can be used to propose a new model. This model is used to examine the efficiency of decision tree and the proposed model of decision tree with artificial neural network, in order to get the desired output.