Abstract
The paper presents a new approach to the problem of pattern recognition. First of all, here is emphasized that the problem itself is fuzzy enough. Later three following novelties of the approach are disclosed: 1) the rule-based fuzzy inference, concerning the measure of patterns’ similarity, is enriched by an idea of hyperinference; 2) a description of the main pattern recognition process is based on Takagi-Sugeno (T-S) reasoning procedure and 3) rule weights in T-S procedure are defined, solving special linear or piecewise linear programming problem (LPP or PWLPP), constructed according to the certain fuzzy experts’ information. The proposed approach was used successfully for recognition of healthy people and those who suffer from certain illness (for example, an atherosclerosis). The classification was performed according to person’s clinical posturograms (stabilograms). At the end of this paper experimental results are presented as well as acknowledgement to all anonymous participants of the experiments.
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Guarracino, M.R., Jasinevicius, R., Krusinskiene, R., Petrauskas, V. (2013). Fuzzy Hyperinference-Based Pattern Recognition. In: Borgelt, C., Gil, M., Sousa, J., Verleysen, M. (eds) Towards Advanced Data Analysis by Combining Soft Computing and Statistics. Studies in Fuzziness and Soft Computing, vol 285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30278-7_18
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DOI: https://doi.org/10.1007/978-3-642-30278-7_18
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