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Open AccessFeature PaperArticle

Performance Evaluation of Automobile Fuel Consumption Using a Fuzzy-Based Granular Model with Coverage and Specificity

1
Department of Control and Instrumentation Engineering, Chosun University, Gwangju 61452, Korea
2
Department of Electronics Engineering, Chosun University, Gwangju 61452, Korea
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Author to whom correspondence should be addressed.
Symmetry 2019, 11(12), 1480; https://doi.org/10.3390/sym11121480
Received: 18 November 2019 / Revised: 29 November 2019 / Accepted: 2 December 2019 / Published: 4 December 2019
The predictive performance of different granular models (GMs) was compared and analyzed for methods that evenly divide linguistic context in information granulation-based GMs and perform flexible partitioning. GMs are defined by input and output space information transformations using context-based fuzzy C-means clustering. The input space information transformation is directly induced by the output space context. Usually, the output space context is evenly divided. In this paper, the linguistic context was flexibly divided by stochastically distributing data in the output space. Unlike most fuzzy models, this GM yielded information segmentation. Their performance is usually evaluated using the root mean square error, which utilizes the difference between the model's output and ground truth. However, this is inadequate for the performance evaluation of information innovation-based GMs. Thus, the GM performance was compared and analyzed using the linguistic context partitioning by selecting the appropriate performance evaluation method for the GM. The method was augmented by the coverage and specificity of the GMs output as the performance index. For the GM validation, its performance was compared and analyzed using the auto MPG dataset. The GM with flexible partitioning of linguistic context performed better. Performance evaluation using the coverage and specificity of the membership function was validated.
Keywords: granular model (GM); context-based fuzzy C-means (CFCM) clustering; information granulation; performance evaluation method; coverage; specificity granular model (GM); context-based fuzzy C-means (CFCM) clustering; information granulation; performance evaluation method; coverage; specificity
MDPI and ACS Style

Yeom, C.-U.; Kwak, K.-C. Performance Evaluation of Automobile Fuel Consumption Using a Fuzzy-Based Granular Model with Coverage and Specificity. Symmetry 2019, 11, 1480.

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