Obtaining interpretable fuzzy classification rules from medical data

By D. Nauck, Rudolf Kruse. 1999

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Citations in the corpus

Interpretable Decision Sets: A Joint Framework for Description and Prediction (2016, background): “Overall, our framework provides a new approach to interpretable machine learning that balances accuracy, interpretability, and computational efficiency.”
Classification with class imbalance problem: A review (2015, background): “Besides that, the many ways of interpreting fuzzy rules and the defuzzification of output prove to be very challenging to solve especially when there is insufficient expert knowledge to define them [129][130].”
Support vector machines combined with feature selection for breast cancer diagnosis (2009, methodology): “In Pena-Reyes and Sipper (1999), the classification technique used fuzzy-GA method reaching a classification accuracy of 97.36%. In Setiono (2000), the classification was based on a feed forward neural network rule extraction algorithm.”
Breast cancer classification using deep belief networks (2016, methodology): “Nauck and Kruse (1999) obtained 95.06% with neuro-fuzzy techiques.”
EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges (2019, methodology): “Lu et al. [101] used a deep neural network constructed using restricted Boltzmann machines and obtained better accuracy than state-of-the-art methods including CSP and FBCSP. Similarly, using a CNN approach, [58] obtained a better classiﬁcation performance than a FBCSP approach.”
Cartesian genetic programming encoded artificial neural networks: a comparison using three benchmarks (2013, unknown): “NEFCLASS [32] No 4.”
Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling (2008, unknown): “Interestingly, Nauck and Kruse took a view of neuro-fuzzy models as a way of heuristically learning parameters of fuzzy systems from data, and proposed two neuro-fuzzy models NEFCLASS [125,127] and NEFPROX [126], which are not automatic “fuzzy system creators’’.”