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Title of Journal: Int J Mach Learn Cyber

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Abbravation: International Journal of Machine Learning and Cybernetics

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Springer Berlin Heidelberg

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DOI

10.1002/ardp.18621620125

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ISSN

1868-808X

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Cognitive concept learning from incomplete informa

Authors: Yingxiu Zhao Jinhai Li Wenqi Liu Weihua Xu
Publish Date: 2016/06/09
Volume: 8, Issue: 1, Pages: 159-170
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Abstract

Cognitive concept learning is to learn concepts from a given clue by simulating human thought processes including perception attention and thinking In recent years it has attracted much attention from the communities of formal concept analysis cognitive computing and granular computing However the classical cognitive concept learning approaches are not suitable for incomplete information Motivated by this problem this study mainly focuses on cognitive concept learning from incomplete information Specifically we put forward a pair of approximate cognitive operators to derive concepts from incomplete information Then we propose an approximate cognitive computing system to perform the transformation between granular concepts as incomplete information is updated periodically Moreover cognitive processes are simulated based on three types of similarities Finally numerical experiments are conducted to evaluate the proposed cognitive concept learning methods

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