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Fuzzy min–max neural networks: a bibliometric and social network analysis

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Abstract

The amount of digital data in the universe is growing at an exponential rate with the rapid development of digital information, and this reveals new machine learning methods. Learning algorithms using hyperboxes are a subsection of machine learning methods. Fuzzy min–max neural network (FMNN) are one of the most common and advanced methods using hyperboxes. FMNN is a special type of NeuroFuzzy system that combines the artificial neural network and fuzzy set into a common framework. This paper conducts an extensive bibliometric and network analysis of FMNN literature. Two hundred and sixty-two publications are analysed from the period of 1992–2022. Several analyses are realized in order to identify trends, challenges and key points in a more scientific and objective way that affect the development of knowledge in the FMNN domain. It can be seen from bibliometric analysis that there is rapid development in the last 10 years. Social network analysis results show that Chee Peng Lim is the most active author in the network. Besides, the modifications of FMNN are generally developed for classification. However, there are still potential future research opportunities for clustering.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

Thanks are due to the referees for their valuable comments. We also thank Higher Education Council of Turkey for 100/2000 Ph.D. scholarship.

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    Ömer Nedim Kenger & Eren Özceylan

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Kenger, Ö.N., Özceylan, E. Fuzzy min–max neural networks: a bibliometric and social network analysis. Neural Comput & Applic 35, 5081–5111 (2023). https://doi.org/10.1007/s00521-023-08267-9

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