Abstract
The detection of diseases often can be formalized as a decision problem that typically has to be solved merging uncertain information; diagnostic tools, intended to aid the physician in interpreting the data, besides attaining the best possible correct classification rate, should furnish some insight into how the problem has been decided. Fuzzy logic is a well known successful attempt to automatize the human capability to reason with imperfect information; fuzzy systems are rule-based so that they can easily provide motivations for their decisions, after having verified some additional conditions.
In this paper we describe a six-steps data driven methodology to automatically build fuzzy systems with a user defined number of rules; almost each step can be approached using several strategies and we thus describe an implementation of the proposed solution. Then, we test our systems on a well known and widely used data set of features of images of breast masses and, having the number of rules varying, we show results both in terms of correct classification rates and in terms of systems’ confidence in the obtained decisions. Finally, we select the number of rules that produces the most interpretable and trustworthy system; such a system is described in details and tested.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Chen, M.Y., Linkens, D.A.: Rule-base self-generation and simplification for data-driven fuzzy models. Fuzzy Sets and Systems 142(2), 243–265 (2004)
Chen, Y., Yang, B., Abraham, A., Peng, L.: Automatic design of hierarchical takagi-sugeno type fuzzy systems using evolutionary algorithms. IEEE T. Fuzzy Systems 15(3), 385–397 (2007)
Fawcett, T.: An introduction to roc analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)
Ghazavi, S.N., Liao, T.W.: Medical data mining by fuzzy modeling with selected features. Artificial Intelligence in Medicine 43(3), 195–206 (2008)
Guillaume, S.: Designing fuzzy inference systems from data: An interpretability-oriented review. IEEE Transactions on Fuzzy Systems 9(3), 426–443 (2001)
Jang, J.S.R.: Anfis: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics 23(3), 665–685 (1993), http://dx.doi.org/10.1109/21.256541
Klir, G., Yuan, B.: Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice-Hall, Englewood Cliffs (1995)
Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7(1), 1–13 (1975)
Pena-Reyes, C.A., Sipper, M.: A fuzzy-genetic approach to breast cancer diagnosis. Artificial Intelligence in Medicine 17(2), 131–155 (1999)
Rezaee, B., Zarandi, M.F.: Data-driven fuzzy modeling for takagi-sugeno-kang fuzzy system. Information Sciences 180(2), 241–255 (2010)
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics 15(1), 116–132 (1985), http://www.hi.cs.meiji.ac.jp/~takagi/paper/TS-MODEL.tar.gz
Tsipouras, M.G., Exarchos, T.P., Fotiadis, D.I.: A methodology for automated fuzzy model generation. Fuzzy Sets Syst. 159(23), 3201–3220 (2008)
Tsipouras, M.G., Exarchos, T.P., Fotiadis, D.I., Kotsia, A.P., Vakalis, K.V., Naka, K.K., Michalis, L.K.: Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling. IEEE Transactions on Information Technology in Biomedicine 12(4), 447–458 (2008)
Wolberg, W.H., Street, N., Mangasarian, O.L.: UCI machine learning repository (1995), http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 3385–353 (1965)
Zadeh, L.A.: Is there a need for fuzzy logic? Inf. Sci. 178(13), 2751–2779 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
d’Acierno, A., De Pietro, G., Esposito, M. (2011). Data Driven Generation of Fuzzy Systems: An Application to Breast Cancer Detection. In: Rizzo, R., Lisboa, P.J.G. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2010. Lecture Notes in Computer Science(), vol 6685. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21946-7_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-21946-7_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21945-0
Online ISBN: 978-3-642-21946-7
eBook Packages: Computer ScienceComputer Science (R0)