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Stability analysis of incremental concept tree for concept cognitive learning

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Abstract

Incremental concept cognitive learning is an emerging active issue that concerning incremental concept learning and dynamic knowledge processing in the dynamic context environments. As the development of that, how to measure the stability of a concept structure in the view of concept cognitive learning has become an urgent issue need to be solved. Motivated by that, we propose a method to analyze the stability of incremental concept tree (ICT) for concept cognitive learning. First, the incremental concept cognitive learning process is visualized in the form of ICT, and then the structure similarity based on concept subtree and the node similarity based on concept importance are carried out to measure the similarity along the evolution of ICT. At last, the global similarity of ICT is obtained by integrating these two similarity measurements by the regulatory factor according to different scenarios. Some numerical experiments compared with classical tree similarity algorithm were conducted to evaluate the effectiveness of our method. The results show that our method is effective to analyze the stability of concept cognitive learning by measuring the similar of ICT and promising to expand it to the field of incremental concept cognitive learning.

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Availability of data and materials

The datasets used or analyzed during the current study are available from the corresponding author Tao Zhang on reasonable request.

Code availability

The code used to generate results shown in this study is available from the author Tao Zhang upon request.

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Funding

This work was supported by the Natural Science Foundation of Hebei Province (F2020203010), Pre-Research Project of the 13th-Five-Year-Plan on Common Technology (No. 41412040302), Humanities and Social Sciences Foundation of the Ministry of Education of China (No. 19YJA740076), and the National Natural Science Foundation of China (No. 61871465).

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Authors and Affiliations

  1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China

    Tao Zhang, Mei Rong & Haoran Shan

  2. Hebei Key Laboratory of Information Transmission and Signal Processing, Qinhuangdao, 066004, China

    Tao Zhang, Mei Rong & Haoran Shan

  3. College of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, 524088, China

    Mingxin Liu

Authors
  1. Tao Zhang
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  2. Mei Rong
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  3. Haoran Shan
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  4. Mingxin Liu
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Contributions

TZ: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision. MR: Investigation, Methodology, Writing—original draft, Validation. HS: Writing—review and editing, Validation. ML: Supervision, Validation, Funding acquisition.

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Correspondence to Tao Zhang.

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All authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Zhang, T., Rong, M., Shan, H. et al. Stability analysis of incremental concept tree for concept cognitive learning. Int. J. Mach. Learn. & Cyber. 13, 11–28 (2022). https://doi.org/10.1007/s13042-021-01332-6

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  • DOI: https://doi.org/10.1007/s13042-021-01332-6

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