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Learning Decision Tree Using Neural Network for Stability and Flexibility

Ahmad Parijaee Moghaddam, Sajjad Mousavi
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Abstract

There are two model for learning in machine learning: symbolic learning and sub-symbolic learning. decision tree is a symbolic approach that is very comprehensible and interpretable. neural network is sub-symbolic approach that is very stable and flexible. neural network tree is a hybrid learning model with the overall structure being a decision tree and each non-terminal node containing a neural network that try to combine advantage of decision tree and neural network. Experiment result shows that, in general, neural network tree is better than traditional decision tree, because neural network is better than one feature. .

Keywords

Decision Tree; Hybrid Decision Tree; Artificial Neural Network; Machine Learning;

References

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Frontiers in Health Informatics (Official Journal of Iranian Association of Medical Informatics)   ISSN-Online: 2676-7104

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The theme is developed by: Gostaresh Afzar Hamara