Abstract
A Decision tree is a tree-like structure, which works in a transparent manner. But when we talk about exponential data, the decision tree is not recommended, as there may be exceptional situation in which the tree could be forced to fall apart and need to be constructed again. The poor statistical efficiency of decision tree can be nullified using Artificial Neural Networks. But due to hidden layers, Neural Nets do not have the ability to explain how it came to a particular outcome. If decision tree could be used in place, this limitation could be overcome. This paper proposes an idea of removing the limitation of both the models by using them together. In this, first, the Artificial Neural Network was constructed on the dataset, and then, decision tree was constructed. This composite framework provides more efficiency and clarity in solution.
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Rathore, M., Gupta, S. (2021). Composite Framework of Artificial Neural Network and Decision Tree Algorithm for Prediction of Fraudulent Firm Using Audit Dataset. In: Rathore, V.S., Dey, N., Piuri, V., Babo, R., Polkowski, Z., Tavares, J.M.R.S. (eds) Rising Threats in Expert Applications and Solutions. Advances in Intelligent Systems and Computing, vol 1187. Springer, Singapore. https://doi.org/10.1007/978-981-15-6014-9_64
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DOI: https://doi.org/10.1007/978-981-15-6014-9_64
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