Extracting Classification Rules from Artificial Neural Network Trained with Discretized Inputs
| Autor: | Dounia Yedjour |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Discretization Association rule learning Computer Networks and Communications Computer science media_common.quotation_subject Network classification Fidelity Computational intelligence 02 engineering and technology Machine learning computer.software_genre Task (project management) 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering media_common Artificial neural network business.industry General Neuroscience Medical decision making ComputingMethodologies_PATTERNRECOGNITION 020201 artificial intelligence & image processing Artificial intelligence business computer Software |
| Zdroj: | Neural Processing Letters. 52:2469-2491 |
| ISSN: | 1573-773X 1370-4621 |
| DOI: | 10.1007/s11063-020-10357-x |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink