Research on Rules Extraction from Neural Network based on Linear Insertion

Wang, Jianguo; Zhang, Wenxing; Qin, Bo; Shi, Wei

doi:10.1109/icie.2010.103

Cited by 1 publication

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the literature, there are many different approaches for the rule extraction from ANNs. A number of algorithms for extracting rules from trained ANNs have been developed in the last two decades [ 21 – 30 ]. Saito and Nakano proposed a medical diagnosis expert system based on a multilayer ANN in [ 21 ].…”

Section: Related Workmentioning

confidence: 99%

“…They compare three Pareto-based approaches to the extraction of neural ensembles and indicate that the method by trading off accuracy and complexity can provide reliable results. Finally, Wang et al proposed a novel algorithm of regression rules extraction from ANN in [ 30 ], which is based on linear intelligent insertion. The linear function and symbolic rules are used to the ANN, and the rules are generated by the decision tree.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A New Data Mining Scheme Using Artificial Neural Networks

Kamruzzaman

Sarkar

2011

Sensors

View full text Add to dashboard Cite

Classification is one of the data mining problems receiving enormous attention in the database community. Although artificial neural networks (ANNs) have been successfully applied in a wide range of machine learning applications, they are however often regarded as black boxes, i.e., their predictions cannot be explained. To enhance the explanation of ANNs, a novel algorithm to extract symbolic rules from ANNs has been proposed in this paper. ANN methods have not been effectively utilized for data mining tasks because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by human experts. With the proposed approach, concise symbolic rules with high accuracy, that are easily explainable, can be extracted from the trained ANNs. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and the accuracy. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of benchmark data mining classification problems.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%