Abstract
Theoretical advances in machine learning have been reflected in many research implementations including in safety-critical domains such as medicine. However this has not been reflected in a large number of practical applications used by domain experts. This bottleneck is in a significant part due to lack of interpretability of the non-linear models derived from data. This lecture will review five broad categories of interpretability in machine learning - nomograms, rule induction, fuzzy logic, graphical models & topographic mapping. Links between the different approaches will be made around the common theme of designing interpretability into the structure of machine learning models, then using the armoury of advanced analytical methods to achieve generic non-linear approximation capabilities.
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
Lisboa, P.J.G.: Industrial use of safety-related artificial neural networks. HSE CR 237/2001, HMSO (2001), http://www.hse.gov.uk/research/crr_pdf/2001/crr01327.pdf
Lisboa, P.J.G.: A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Networks 15(1), 9–37 (2002)
Lisboa, P.J.G., Taktak, A.F.G.: The use of artificial neural networks in decision support in cancer: A systematic review. Neural Networks 19(4), 408–415 (2006)
Chiu, S.: Developing commercial applications of intelligent control. IEEE Control Syst. Mag. 17(2), 94–100 (1997)
Vellido, A., Lisboa, P.J.G.: Handling outliers in brain tumour MRS data analysis through robust topographic mapping. Computers in Biology and Medicine 36(10), 1049–1063 (2006)
Van Belle, V., Lisboa, P.J.G.: Research Directions in Interpretable Machine Learning. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, April 24-26, pp. 191–196 (2013)
Breiman, L.: Statistical Modeling: The Two Cultures. Statistical Science 16(3), 199–231 (2001)
Bacciu, D., Lisboa, P.J.G., Sperdutti, A., Villmann, T.: Probabilistic Modelling in Machine Learning. In: Alippi, C., et al. (eds.) Handbook on Computational Intelligence. Springer (accepted, 2013)
Lisboa, P.J.G., Ellis, I.O., Green, A.R., Ambrogi, F., Dias, M.B.: Cluster-based visualisation with scatter matrices. Pattern Recognition Letters 29(13), 1814–1823 (2008)
Bartholomew, Knott, Moustaki: Latent Variable Models and Factor Analysis: A Unified Approach, 3rd edn. (2011)
Gorban, A.N., Zinovyev, A.: Principal manifolds and graphs in practice: from molecular biology to dynamical systems. International Journal of Neural Systems 20(3), 219–232 (2010)
Etchells, T.A., Lisboa, P.J.G.: Orthogonal search-based rule extraction (OSRE) from trained neural networks: A practical and efficient approach. IEEE Transactions on Neural Networks 17(2), 374–384 (2006)
Rögnvaldsson, T., Etchells, T.A., You, L., Garwicz, D., Jarman, I.H., Lisboa, P.J.G.: How to find simple and accurate rules for viral protease cleavage specificities. BMC Bioinformatics 10, 149 (2009)
Lisboa, P.J.G., Etchells, T.A., Pountney, D.C.: Minimal MLPs do not model the XOR logic. Neurocomputing, Rapid Communication 48(1-4), 1033–1037 (2002)
Jarman, I.H., Etchells, T.A., Martín, J.D., Lisboa, P.J.G.: An integrated framework for risk profiling of breast cancer patients following surgery. Artificial Intelligence in Medicine 42, 165–188 (2008)
Bacciu, D., Etchells, T.A., Lisboa, P.J.G., Whittaker, J.: Efficient identification of independence networks using mutual information. Computational Statistics 28(2), 621–646 (2013)
Fernandez, F., Duarte, A., Sanchez, A.: Optimization of the Fuzzy Partition of a Zero-order Takagi-Sugeno Model. In: Proc. Eleventh International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU 2006), vol. I, pp. 898–905. Editions EDK (2006)
López de Mántaras, R., McSherry, D., Bridge, D., Leake, D., Smyth, B., Craw, S., Faltings, B., Maher, M.-L., Cox, M., Forbus, K., Keane, M., Aamodt, A., Watson, I.: Retrieval, reuse, revision, and retention in case-based reasoning. Knowledge Engineering Review 20(3), 215–240 (2005)
Dutta, S., Bonissone, P.: Integrating Case Based And Rule Based Reasoning: The Possibilistic Connection. In: Proc. 6th Conference on Uncertainty in AI, Cambridge, MA, July 27-29, pp. 290–300 (1990)
Van Belle, V., Lisboa, P.J.G.: Automated Selection of Interaction Effects in Sparse Kernel Methods to Predict Pregnancy Viability. In: IEEE Symposium Series on Computational Intelligence, Singapore, April 16-19 (2013)
Ruiz, H., Etchells, T.A., Jarman, I.H., Martín, J.D., Lisboa, P.J.G.: A principled approach to network-based classification and data representation. Neurocomputing 112, 79–91 (2013)
Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA 103(23), 8577–8582 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Lisboa, P.J.G. (2013). Interpretability in Machine Learning – Principles and Practice . In: Masulli, F., Pasi, G., Yager, R. (eds) Fuzzy Logic and Applications. WILF 2013. Lecture Notes in Computer Science(), vol 8256. Springer, Cham. https://doi.org/10.1007/978-3-319-03200-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-03200-9_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03199-6
Online ISBN: 978-3-319-03200-9
eBook Packages: Computer ScienceComputer Science (R0)