Abstract
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.
Highlights
Fuzzy modeling seeks to develop relationships between fuzzy sets or information granulations considered as fuzzy relations
We evaluated the relation of a fuzzy set generated in the granular models (GMs)’s input and output spaces using performance evaluation methods, which utilize coverage and specificity, rather than using general performance evaluation methods, such as mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE)
We compared and analyzed the predictive performances of linguistic context segmentation methods of GMs constructed by information granulation
Summary
Fuzzy modeling seeks to develop relationships between fuzzy sets or information granulations considered as fuzzy relations. Structures, and algorithms have been explored in the field of fuzzy modeling. Das [1] proposed an evolutionary interval type-2 neural fuzzy inference system (IT2FIS), based on the Takagi–Sugeno–Kang fuzzy inference system and a completely sequential learning algorithm. Jang [2] proposed an adaptive neuro-fuzzy inference system by fusing a fuzzy inference system and an artificial neural network. Zhang [3] proposed a new fuzzy logic system (FLS) modeling framework, termed the “data-driven elastic FLS” (DD-EFLS). Alizadeh [4] proposed an eigen fuzzy inference system (eHFIS) that can simultaneously perform local input selection and system identification of a fuzzy inference system. Cevantes [5] proposed a neuro-fuzzy system that implements differential neural networks (DNNs) using the Takagi–Sugeno (T-S) fuzzy inference rules
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