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Extracting Classification Rules from Artificial Neural Network Trained with Discretized Inputs

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Abstract

Rule extraction from artificial neural networks remains important task in complex diseases such as diabetes and breast cancer where the rules should be accurate and comprehensible. The quality of rules is improved by the improvement of the network classification accuracy which is done by the discretization of input attributes. In this paper, we developed a rule extraction algorithm based on multiobjective genetic algorithms and association rules mining to extract highly accurate and comprehensible classification rules from ANN’s that have been trained using the discretization of the continuous attributes. The data pre-processing provides very good improvement of the ANN accuracy and consequently leads to improve the performance of the classification rules in terms of fidelity and coverage. The results show that our algorithm is very suitable for medical decision making, so an excellent average accuracy of 94.73 has been achieved for the Pima dataset and 99.36 for the breast cancer dataset.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Mathematics and Computer Science, Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf, USTO-MB, BP 1505, El M’naouer, 31000, Oran, Algeria

    Dounia Yedjour

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Yedjour, D. Extracting Classification Rules from Artificial Neural Network Trained with Discretized Inputs. Neural Process Lett 52, 2469–2491 (2020). https://doi.org/10.1007/s11063-020-10357-x

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