Sensors 2011, 11(5), 4622-4647; doi:10.3390/s110504622

A New Data Mining Scheme Using Artificial Neural Networks

1,* email and 2email
Received: 11 February 2011; in revised form: 11 April 2011 / Accepted: 14 April 2011 / Published: 28 April 2011
(This article belongs to the Section Physical Sensors)
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Abstract: 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.
Keywords: data mining; neural networks; symbolic rules; weight freezing; constructive algorithm; pruning; clustering; rule extraction; symbolic rules
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MDPI and ACS Style

Kamruzzaman, S.M.; Sarkar, A.M.J. A New Data Mining Scheme Using Artificial Neural Networks. Sensors 2011, 11, 4622-4647.

AMA Style

Kamruzzaman SM, Sarkar AMJ. A New Data Mining Scheme Using Artificial Neural Networks. Sensors. 2011; 11(5):4622-4647.

Chicago/Turabian Style

Kamruzzaman, S. M.; Sarkar, A. M. Jehad. 2011. "A New Data Mining Scheme Using Artificial Neural Networks." Sensors 11, no. 5: 4622-4647.

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