Rule Extraction from Training Data Using Neural Network
| Autor: | Pinki Roy, Biswajit Purkayastha, Dalton Meitei Thounaojam, Manomita Chakraborty, Saroj Kumar Biswas |
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| Rok vydání: | 2016 |
| Předmět: |
Reverse engineering
Training set Artificial neural network business.industry Time delay neural network Computer science 02 engineering and technology Machine learning computer.software_genre Probabilistic neural network ComputingMethodologies_PATTERNRECOGNITION Artificial Intelligence 020204 information systems Black box 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Data mining White box business computer FSA-Red Algorithm |
| Zdroj: | International Journal on Artificial Intelligence Tools. 26:1750006 |
| ISSN: | 1793-6349 0218-2130 |
| Popis: | Data Mining is a powerful technology to help organization to concentrate on most important data by extracting useful information from large database. One of the most commonly used techniques in data mining is Artificial Neural Network due to its high performance in many application domains. Despite many advantages of Artificial Neural Network, one of its main drawbacks is its inherent black box nature which is the main problem of using Artificial Neural Network in data mining. Therefore, this paper proposes a rule extraction algorithm from neural network using classified and misclassified data to convert the black box nature of Artificial Neural Network into a white box. The proposed algorithm is a modification of the existing algorithm, Rule Extraction by Reverse Engineering (RxREN). The proposed algorithm extracts rules from trained neural network for datasets with mixed mode attributes using pedagogical approach. The proposed algorithm uses both classified as well as misclassified data to find out the data ranges of significant attributes in respective classes, which is the innovation of the proposed algorithm. The experimental results clearly show that the performance of the proposed algorithm is superior to existing algorithms. |
| Databáze: | OpenAIRE |
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