Building Interpretable Systems in Real Time

This chapter contains sections titled: Introduction Improving eTS Span in the Reachable State Space Pruning the Rule Base Kernel Machines Experimental Results Conclusion Acknowledgments References

[1]  Hisao Ishibuchi,et al.  Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning , 2007, Int. J. Approx. Reason..

[2]  Jesús Alcalá-Fdez,et al.  Genetic learning of accurate and compact fuzzy rule based systems based on the 2-tuples linguistic representation , 2007, Int. J. Approx. Reason..

[3]  Satinder P. Singh,et al.  Kernel Predictive Linear Gaussian models for nonlinear stochastic dynamical systems , 2006, ICML.

[4]  Jason Weston,et al.  Fast Kernel Classifiers with Online and Active Learning , 2005, J. Mach. Learn. Res..

[5]  Shie Mannor,et al.  The kernel recursive least-squares algorithm , 2004, IEEE Transactions on Signal Processing.

[6]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[7]  Nikola K. Kasabov,et al.  DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction , 2002, IEEE Trans. Fuzzy Syst..

[8]  Giovanna Castellano,et al.  Distinguishability quantification of fuzzy sets , 2007, Inf. Sci..

[9]  Ignacio Santamaría,et al.  A Sliding-Window Kernel RLS Algorithm and Its Application to Nonlinear Channel Identification , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[10]  Bernard De Baets,et al.  Interpretability-preserving genetic optimization of linguistic terms in fuzzy models for fuzzy ordered classification: An ecological case study , 2007, Int. J. Approx. Reason..

[11]  Oliver Nelles,et al.  LOLIMOT - Lokale, lineare Modelle zur Identifikation nichtlinearer, dynamischer Systeme , 1997 .

[12]  Héctor Pomares,et al.  Improving the accuracy while preserving the interpretability of fuzzy function approximators by means of multi-objective evolutionary algorithms , 2007, Int. J. Approx. Reason..

[13]  Anukool Lakhina,et al.  Multivariate Online Anomaly Detection Using Kernel Recursive Least Squares , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[14]  Uzay Kaymak,et al.  Similarity measures in fuzzy rule base simplification , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[15]  Ralf Mikut,et al.  Interpretability issues in data-based learning of fuzzy systems , 2005, Fuzzy Sets Syst..

[16]  Ronald R. Yager,et al.  Learning of Fuzzy Rules by Mountain Clustering , 1992 .

[17]  J. Casillas Interpretability issues in fuzzy modeling , 2003 .

[18]  A. Dourado,et al.  On the complexity and interpretability of support vector machines for process modeling , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[19]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[20]  Yaochu Jin,et al.  Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement , 2000, IEEE Trans. Fuzzy Syst..

[21]  D.P. Filev,et al.  An approach to online identification of Takagi-Sugeno fuzzy models , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[22]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[23]  Feng Liu,et al.  A Novel Generic Hebbian Ordering-Based Fuzzy Rule Base Reduction Approach to Mamdani Neuro-Fuzzy System , 2007, Neural Computation.

[24]  Daniel A. Keim,et al.  Information Visualization and Visual Data Mining , 2002, IEEE Trans. Vis. Comput. Graph..

[25]  Antonio F. Gómez-Skarmeta,et al.  Improving interpretability in approximative fuzzy models via multiobjective evolutionary algorithms , 2007, EUSFLAT Conf..

[26]  Plamen P. Angelov,et al.  Data-driven evolving fuzzy systems using eTS and FLEXFIS: comparative analysis , 2008, Int. J. Gen. Syst..

[27]  Bernhard Sendhoff,et al.  Extracting Interpretable Fuzzy Rules from RBF Networks , 2003, Neural Processing Letters.

[28]  Plamen P. Angelov,et al.  Fuzzy systems design: direct and indirect approaches , 2006, Soft Comput..

[29]  John Q. Gan,et al.  Constructing accurate and parsimonious fuzzy models with distinguishable fuzzy sets based on an entropy measure , 2006, Fuzzy Sets Syst..