A General Neural Framework for Classification Rule Mining

A General Neural Framework for Classification Rule Mining (2001)

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by Zhi-hua Zhou , Yuan Jiang , Shi-fu Chen

Venue:	International Journal of Computers, Systems and Signals
Citations:	1 - 0 self

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@ARTICLE{Zhou01ageneral,
    author = {Zhi-hua Zhou and Yuan Jiang and Shi-fu Chen},
    title = {A General Neural Framework for Classification Rule Mining},
    journal = {International Journal of Computers, Systems and Signals},
    year = {2001},
    volume = {1},
    pages = {154--168}
}

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Abstract

Abstract. Neural network technology has already been applied in a variety of domains with remarkable success. However, it has not been well utilized in data mining and knowledge discovery. In this paper, a general neural framework named NEUCRUM is proposed for classiÞcation rule mining. This paper also presents a possible implementation of NEUCRUM whose key components are a speciÞc neural classiÞer named FANNC and a novel rule extraction approach named STARE.FANNC is used to learn from pre-processed data, in which its fast learning speed and strong generalization ability are quite contributive. STARE is proposed in this paper, which is used to extract comprehensible, compact and accurate symbolic rules from trained neural networks so that the knowledge discovered is explicitly available to decision-makers. Applications show that NEUCRUM and its implementation described in this paper work well in many real domains.

Citations

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