Gath-Geva specification and genetic generalization of Takagi-Sugeno-Kang fuzzy models

This paper introduces a fuzzy inference system, based on the Takagi-Sugeno-Kang model, to achieve efficient and reliable classification in the domain of ubiquitous computing, and in particular for smart or context-aware, sensor-augmented devices. As these are typically deployed in unpredictable environments and have a large amount of correlated sensor data, we propose to use a Gath-Geva clustering specification as well as a genetic algorithm approach to improve the model's robustness. Experiments on data from such a sensor-augmented device show that accuracy is boosted from 83% to 97% with these optimizations under normal conditions, and for more. challenging data from 54% to 79%.

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