Interpretability of fuzzy systems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8256 LNAI, Issue , 2013, Pages 22-35

  Mencar, Corrado ^a  

^a UNIVERSITY OF BARI   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

DATA-DRIVEN DESIGN; DESIGN OF FUZZY SYSTEMS; INTERPRETABILITY; LINGUISTIC KNOWLEDGE; LINGUISTIC RULES; NONLINEAR BEHAVIOURS; REAL-WORLD; STATE OF RESEARCH;

FUZZY LOGIC; LINGUISTICS;

FUZZY SYSTEMS;

EID: 84893729567     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-03200-9_3     Document Type: Conference Paper

References (37)

1. Zadeh, L.A.: Is there a need for fuzzy logic? Information Sciences 178(13), 2751-2779 (2008)
2. Michalski, R.S.: A theory and methodology of inductive learning. Artificial Intelligence 20, 111-161 (1983)
3. Miller, G.A.: The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information. The Psychological Review 63, 81-97 (1956)
4. Mencar, C., Fanelli, A.M.: Interpretability constraints for fuzzy information granulation. Information Sciences 178(24), 4585-4618 (2008)
5. Zhou, S., Gan, J.: Low-level interpretability and high-level interpretability: A unified view of data-driven interpretable fuzzy system modelling. Fuzzy Sets and Systems 159(23), 3091-3131 (2008)
6. Alonso, J.M., Magdalena, L., González-Rodríguez, G.: Looking for a good fuzzy system interpretability index: An experimental approach. International Journal of Approximate Reasoning 51(1), 115-134 (2009)
7. Gacto, M.J., Alcalá, R., Herrera, F.: Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures. Information Sciences 181(20), 4340-4360 (2011)
8. Botta, A., Lazzerini, B., Marcelloni, F., Stefanescu, D.C.: Context adaptation of fuzzy systems through a multi-objective evolutionary approach based on a novel interpretability index. Soft Computing 13(5), 437-449 (2009)
9. Gacto, M.J., Alcalá, R., Herrera, F.: Integration of an index to preserve the semantic interpretability in the multiobjective evolutionary rule selection and tuning of linguistic fuzzy systems. IEEE Transactions on Fuzzy Systems 18(3), 515-531 (2010)
10. Mencar, C., Castiello, C., Cannone, R., Fanelli, A.M.: Interpretability assessment of fuzzy knowledge bases: A cointension based approach. International Journal of Approximate Reasoning 52(4), 501-518 (2011)
11. Alonso, J.M., Pancho, D.P., Cordón, O., Quirin, A., Magdalena, L.: Social network analysis of co-fired fuzzy rules. In: Yager, R.R., Abbasov, A.M., Reformat, M., Shahbazova, S.N. (eds.) Soft Computing: State of the Art Theory. STUDFUZZ, vol. 291, pp. 113-128. Springer, Heidelberg (2013)
12. Casillas, J., Cordón, O., Herrera, F., Magdalena, L.: Interpretability issues in fuzzy modeling. STUDFUZZ, vol. 128. Springer, Heidelberg (2003)
13. Casillas, J., Cordón, O., Herrera, F., Magdalena, L.: Accuracy improvements in linguistic fuzzy modeling. STUDFUZZ, vol. 129. Springer, Heidelberg (2003)
14. Riid, A., Rüstern, E.: Identification of transparent, compact, accurate and reliable linguistic fuzzy models. Information Sciences 181(20), 4378-4393 (2011)
15. Fazzolari, M., Alcalá, R., Nojima, Y., Ishibuchi, H., Herrera, F.: A review of the application of multi-objective evolutionary fuzzy systems: Current status and further directions. IEEE Transactions on Fuzzy Systems 21(1), 45-65 (2013)
16. Ducange, P., Marcelloni, F.: Multi-objective evolutionary fuzzy systems. In: Fanelli, A.M., Pedrycz, W., Petrosino, A. (eds.)WILF 2011. LNCS (LNAI), vol. 6857, pp. 83-90. Springer, Heidelberg (2011)
17. Lucarelli, M., Castiello, C., Fanelli, A.M., Mencar, C.: Automatic Design of Interpretable Fuzzy Partitions with Variable Granularity: An Experimental Comparison. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part I. LNCS, vol. 7894, pp. 318-328. Springer, Heidelberg (2013)
18. Cannone, R., Castiello, C., Mencar, C., Fanelli, A.M.: A Study on Interpretability Conditions for Fuzzy Rule-Based Classifiers. In: IEEE Ninth International Conference on Intelligent Systems Design and Applications, ISDA 2009, Pisa, Italy, pp. 438-443 (November 2009)
19. Pulkkinen, P., Hytonen, J., Koivisto, H.: Developing a bioaerosol detector using hybrid genetic fuzzy systems. Engineering Applications of Artificial Intelligence 21(8), 1330-1346 (2008)
20. Vanbroekhoven, E., Adriaenssens, V., Debaets, B.: Interpretability- preserving genetic optimization of linguistic terms in fuzzy models for fuzzy ordered classification: An ecological case study. International Journal of Approximate Reasoning 44(1), 65-90 (2007)
21. Guillaume, S., Charnomordic, B.: Interpretable fuzzy inference systems for cooperation of expert knowledge and data in agricultural applications using FisPro. In: IEEE International Conference on Fuzzy Systems, pp. 2019-2026 (2010)
22. Gadaras, I., Mikhailov, L.: An interpretable fuzzy rule-based classification methodology for medical diagnosis. Artificial Intelligence in Medicine 47(1), 25-41 (2009)
23. Alonso, J.M., Castiello, C., Lucarelli, M., Mencar, C.: Modelling interpretable fuzzy rule-based classifiers for medical decision support. In: Magdalena, R., Soria, E., Guerrero, J., Gómez-Sanchis, J., Serrano, A. (eds.) Medical Applications of Intelligent Data Analysis: Research Advancements, pp. 254-271. IGI Global (2012)
24. Carmona, C.J., Gonzalez, P., del Jesus, M.J., Navio-Acosta, M., Jimenez-Trevino, L.: Evolutionary fuzzy rule extraction for subgroup discovery in a psychiatric emergency department. Soft Computing 15(12), 2435-2448 (2011)
25. Kumar, A.: Interpretability and mean-square error performance of fuzzy inference systems for data mining. Intelligent Systems in Accounting, Finance and Management 13(4), 185-196 (2005)
26. Cheong, F.: A hierarchical fuzzy system with high input dimensions for forecasting foreign exchange rates. International Journal of Artificial Intelligence and Soft Computing 1(1), 15 (2008)
27. Ghandar, A., Michalewicz, Z., Zurbruegg, R.: Enhancing profitability through interpretability in algorithmic trading with a multiobjective evolutionary fuzzy system. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012, Part II. LNCS, vol. 7492, pp. 42-51. Springer, Heidelberg (2012)
28. Altug, S., Chow, M.Y., Trussell, H.J.: Heuristic constraints enforcement for training of and rule extraction from a fuzzy/neural architecture. II. Implementation and application. IEEE Transactions on Fuzzy Systems 7(2), 151-159 (1999)
29. Riid, A., Rustern, E.: Interpretability of fuzzy systems and its application to process control. In: IEEE International Conference on Fuzzy Systems, pp. 1-6 (2007)
30. Alonso, J.M., Ocaña, M., Hernandez, N., Herranz, F., Llamazares, A., Sotelo, M.A., Bergasa, L.M., Magdalena, L.: Enhanced WiFi localization system based on Soft Computing techniques to deal with small-scale variations in wireless sensors. Applied Soft Computing 11(8), 4677-4691 (2011)
31. Alonso, J.M., Magdalena, L., Guillaume, S., Sotelo, M.A., Bergasa, L.M., Ocaña, M., Flores, R.: Knowledge-based intelligent diagnosis of ground robot collision with non detectable obstacles. Journal of Intelligent and Robotic Systems 48(4), 539-566 (2007)
32. Mucientes, M., Casillas, J.: Quick design of fuzzy controllers with good interpretability in mobile robotics. IEEE Transactions on Fuzzy Systems 15(4), 636-651 (2007)
33. Barrientos, F., Sainz, G.: Interpretable knowledge extraction from emergency call data based on fuzzy unsupervised decision tree. Knowledge-Based Systems 25(1), 77-87 (2011)
34. Troiano, L., Rodríguez-Muñiz, L.J., Ranilla, J., Díaz, I.: Interpretability of fuzzy association rules as means of discovering threats to privacy. International Journal of Computer Mathematics 89(3), 325-333 (2012)
35. Bargiela, A., Pedrycz, W.: Granular computing: an introduction. Kluwer Academic Publishers, Boston (2003)
36. Alonso, J., Cordon, O., Quirin, A., Magdalena, L.: Analyzing interpretability of fuzzy rule-based systems by means of fuzzy inference-grams. In: 1st World Conference on Soft Computing, San Francisco, CA, USA, pp. 181.1-181.8 (2011)
37. Bargiela, A., Pedrycz, W.: Human-Centric Information Processing Through Granular Modelling. Springer Publishing Company, Incorporated (2009)