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International Journal of Machine Learning and Cybernetics

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09-06-2016 | Original Article

Cognitive concept learning from incomplete information

Authors: Yingxiu Zhao, Jinhai Li, Wenqi Liu, Weihua Xu

Published in: International Journal of Machine Learning and Cybernetics | Issue 1/2017

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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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Title: Cognitive concept learning from incomplete information
Authors: Yingxiu Zhao
Jinhai Li
Wenqi Liu
Weihua Xu
Publication date: 09-06-2016
Publisher: Springer Berlin Heidelberg
Published in: International Journal of Machine Learning and Cybernetics / Issue 1/2017
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI: https://doi.org/10.1007/s13042-016-0553-8

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