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- W4313362798 abstract "In domains that have complex data characteristics and/or noisy data, any single supervised learning algorithm tends to suffer from overfitting. One way to mitigate this problem is to combine unsupervised learning component as a front end of the main supervised learner. In this paper, we propose a hierarchical combination of fuzzy C-means clustering component and fuzzy max–min neural network supervised learner for that purpose. The proposed method is evaluated in a noisy domain (Pima Indian Diabetes open database). The proposed combination showed superior result to standalone fuzzy max–min and backpropagation-based neural network. The proposed method also showed better performance than any single supervised learner tested in the same domain in the literature with high accuracy (80.96%) and was at least competitive in other measures such as sensitivity, specificity, and F1 measure." @default.
- W4313362798 created "2023-01-06" @default.
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- W4313362798 date "2022-12-27" @default.
- W4313362798 modified "2023-10-14" @default.
- W4313362798 title "Combining Supervised and Unsupervised Fuzzy Learning Algorithms for Robust Diabetes Diagnosis" @default.
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- W4313362798 doi "https://doi.org/10.3390/app13010351" @default.
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