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Attribute-oriented cognitive concept learning strategy: a multi-level method

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Abstract

Recently, formal concept analysis has become a potential direction of cognitive computing, which can describe the processes of cognitive concept learning. We establish a concept hierarchy structure based on the existing cognitive concept learning methods. However, none of these methods could obtain the following results: get the concept, recognize objects and distinguish between two different objects. In this paper, our focus is to construct an attribute-oriented multi-level cognitive concept learning method so as to improve and enhance the ability of cognitive concept learning. Firstly, the view point of human cognition is discussed from the multi-level approach, and then the mechanism of attribute-oriented cognitive concept learning is investigated. Through some defined special attributes, we propose a corresponding structure of attribute-oriented multi-level cognitive concept learning from an interdisciplinary viewpoint. It is a combination of philosophy and psychology of human cognition. Moreover, to make the presented attribute-oriented multi-level method easier to understand and apply in practice, an algorithm of cognitive concept learning is established. Furthermore, a case study about how to recognize the real-world animals is studied to use the proposed method and theory. Finally, in order to solve conceptual cognition problems, we perform an experimental evaluation on five data sets downloaded from the University of California-Irvine (UCI) databases. And then we provide a comparative analysis with the existing \(granular\ computing\ approach\ to\ two\)-\(way\ learning\) [44] and the three-\(way\ cognitive\ concept\ learning\ via\ multi\)-granularity [9]. We obtain more number of concepts than \(the\ two\)-\(way\ learning\ and\ the\ three\)-\(way\ cognitive\ concept\ learning\ approaches\), which shows the feasibility and effectiveness of our attribute-oriented multi-level cognitive learning method.

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Acknowledgements

This work is supported by the Macau Science and Technology Development Fund (No. 081/2015/A3), the National Natural Science Foundation of China (No. 71471060, No. 61472463, and No. 61772002), the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJ1709221).

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Authors and Affiliations

  1. Faculty of Information Technology, Macau University of Science and Technology, Taipa, Macau, People’s Republic of China

    Bingjiao Fan & Eric C. C. Tsang

  2. School of Mathematics and Statistics, Southwest University, Chongqing, 400715, People’s Republic of China

    Weihua Xu

  3. Department of Mathematics and Physics, North China Electric Power University, Beijing, 102206, People’s Republic of China

    Degang Chen

  4. Department of Mathematics, Harbin Institute of Technology, Harbin, 150001, People’s Republic of China

    Wentao Li

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  1. Bingjiao Fan
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Correspondence to Eric C. C. Tsang.

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Fan, B., Tsang, E.C.C., Xu, W. et al. Attribute-oriented cognitive concept learning strategy: a multi-level method. Int. J. Mach. Learn. & Cyber. 10, 2421–2437 (2019). https://doi.org/10.1007/s13042-018-0879-5

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