A Preliminary Study on Symbolic Fuzzy Cognitive Maps for Pattern Classification
Communications in Computer and Information Science, ISSN: 1865-0937, Vol: 1052, Page: 285-295
2019
- 1Citations
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Conference Paper Description
Within the neural computing field, Fuzzy Cognitive Maps (FCMs) are attractive simulation tools to model dynamic systems by means of well-defined neural concepts and causal relationships, thus equipping the network with interpretability features. However, such components are normally described by quantitative terms, which may be difficult to handle by experts. Recently, we proposed a symbolic FCM scheme (termed FCM-TFN) in which both weights and activation values are described by triangular fuzzy numbers. In spite of the promising results, the model’s performance in solving prediction problems remains uncertain. In this paper, we explore the prediction capabilities of the FCM-TFN model in pattern classification and concluded that our method is able to perform well when compared with traditional classifiers.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85075701384&origin=inward; http://dx.doi.org/10.1007/978-3-030-31019-6_25; http://link.springer.com/10.1007/978-3-030-31019-6_25; http://link.springer.com/content/pdf/10.1007/978-3-030-31019-6_25; https://dx.doi.org/10.1007/978-3-030-31019-6_25; https://link.springer.com/chapter/10.1007/978-3-030-31019-6_25
Springer Science and Business Media LLC
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