TY  - BOOK
AU  - Kenesei,Tamás
AU  - Abonyi,János
ED  - SpringerLink (Online service)
TI  - Interpretability of Computational Intelligence-Based Regression Models
T2  - SpringerBriefs in Computer Science,
SN  - 9783319219424
AV  - Q334-342 
PY  - 2015///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Artificial intelligence
KW  - Engineering
KW  - Data mining
KW  - Artificial Intelligence
KW  - Computational Intelligence
KW  - Data Mining and Knowledge Discovery
N1  - Introduction -- Interpretability of Hinging Hyperplanes -- Interpretability of Neural Networks -- Interpretability of Support Vector Machines -- Summary
N2  - The key idea of this book is that hinging hyperplanes, neural networks and support vector machines can be transformed into fuzzy models, and interpretability of the resulting rule-based systems can be ensured by special model reduction and visualization techniques. The first part of the book deals with the identification of hinging hyperplane-based regression trees. The next part deals with the validation, visualization and structural reduction of neural networks based on the transformation of the hidden layer of the network into an additive fuzzy rule base system. Finally, based on the analogy of support vector regression and fuzzy models, a three-step model reduction algorithm is proposed to get interpretable fuzzy regression models on the basis of support vector regression. The authors demonstrate real-world use of the algorithms with examples taken from process engineering, and they support the text with downloadable Matlab code. The book is suitable for researchers, graduate students and practitioners in the areas of computational intelligence and machine learning
UR  - https://doi.org/10.1007/978-3-319-21942-4
ER  -