Abstract
We extend a procedure based on support vector clustering and devoted to inferring the membership function of a fuzzy set to the case of a universe of discourse over which several fuzzy sets are defined. The extended approach learns simultaneously these sets without requiring as previous knowledge either their number or labels approximating membership values. This data-driven approach is completed via expert knowledge incorporation in the form of predefined shapes for the membership functions. The procedure is successfully tested on a benchmark.
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Notes
- 1.
Note that even with this careful setting, there is no guarantee that the three clusters will get associated injectively with the three available classes. We simply re-executed the iterations in which these cases occurred.
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Acknowledgements
The authors would like to thank Angelo Ciaramella and Antonino Staiano for the fruitful discussion concerning the organization of the experimental phase.
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Cermenati, L., Malchiodi, D., Zanaboni, A.M. (2020). Simultaneous Learning of Fuzzy Sets. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Neural Approaches to Dynamics of Signal Exchanges. Smart Innovation, Systems and Technologies, vol 151. Springer, Singapore. https://doi.org/10.1007/978-981-13-8950-4_16
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