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周志华

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期刊论文

Extracting symbolic rules from trained neural network ensembles

周志华Zhi-Hua Zhou* Yuan Jiang and Shi-Fu Chen

AI Communications 16 (2003) 3-15,-0001,():

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摘要/描述

Neural network ensemble can significantly improve the generalization ability of neural network based systems. However, its comprehensibility is even worse than that of a single neural network because it comprises a collection of individual neural networks. In this paper, an approach named REFNE is proposed to improve the comprehensibility of trained neural network ensembles that perform classification tasks. REFNE utilizes the trained ensembles to generate instances and then extracts symbolic rules from those instances. It gracefully breaks the ties made by individual neural networks in prediction. It also employs specific discretization scheme, rule form, and fidelity evaluation mechanism. Experiments show that with different configurations, REFNE can extract rules with good fidelity that well explain the function of trained neural network ensembles, or rules with strong generalization ability that are even better than the trained neural network ensembles in prediction.

版权说明:以下全部内容由周志华上传于   2005年08月02日 18时08分49秒,版权归本人所有。

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