Abstract
We describe step by step how to design, implement and validate an interpretable fuzzy rule-based beer style classifier endowed with explanation capability. First, we revise some preliminary work regarding both interpretable fuzzy modeling methodologies and related software. Second, we introduce the use case on beer style classification. Third, we build and validate a fuzzy rule-based classifier with a good interpretability-accuracy trade-off for this use case. Fourth, we endow this classifier with explanation capability through a general linguistic interface that is tuned ad-hoc for the use case under consideration. Fifth, we show how the designed explainable classifier can be refined and exploited with several interoperable software tools. Finally, we compare two kinds of multi-modal (i.e., textual and graphical) explanations: (1) explanations which are inherently natural and fully-meaningful to users, because they are supported by an interpretable fuzzy rule-based classifier which is carefully designed (i.e., it is grounded in common-sense expert knowledge and global semantics); and (2) explanations which are supported by the linguistic approximation of a fuzzy rule-based classifier extracted from data with the focus only on accuracy (thus lacking of linguistic interpretability). The use case is implemented with open source software and all related datasets, tools and scripts are available online for the sake of reproducibility.
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Notes
- 1.
European Commission, Artificial Intelligence for Europe, Brussels, Belgium, “Communication from the Commission to the European Parliament, the European Council, the Council, the European Economic and Social Committee and the Committee of the Regions”, Tech. Rep., 2018, (SWD(2018) 137 final) https://ec.europa.eu/digital-single-market/en/news/communication-artificial-intelligence-europe.
- 2.
- 3.
ACM US Public Policy Council: Statement on Algorithmic Transparency and Accountability, 2017, https://www.acm.org/binaries/content/assets/public-policy/2017_usacm_statement_algorithms.pdf.
- 4.
- 5.
GUAJE makes suggestions but the user always makes the final decision. Anyway, unsolved consistency issues can be automatically fixed during the “KB improvement” stage, if the user selects this option.
- 6.
There is some overlapping between the two stages named as “Linguistic rule integration” and “KB improvement”. They may run independently, but if they ran in sequence then tasks (RS.1), (RS.2), (PS.1) and (PS.2) in the “KB improvement” stage would make no sense because the KB should be free of redundant and/or unused elements after fixing consistency issues previously identified during the “Linguistic rule integration” stage.
- 7.
- 8.
- 9.
An illustrative example is provided in the next section (see Fig. 6.14).
- 10.
We adopt the notation given in Alonso et al. (2019), even if this notation is slightly different from the rest of the book. Namely, \(U\) is an input vector while it refers to the universe of discourse in the rest of the book. In addition, \(T\) is a text generation algorithm while the same symbol refers to the set of linguistic terms in the rest of the book.
- 11.
- 12.
SimpleNLG in Spanish: https://github.com/citiususc/SimpleNLG-ES.
- 13.
SimpleNLG in English: https://github.com/simplenlg/.
- 14.
We recommend to install and run GUAJE (Alonso 2020) in order to appreciate all details. GUAJE lets you zoom in and zoom out of the figures provided as screenshots in the rest of this chapter. Thus, you are not to miss any detail when analyzing the given pictures even if they were printed into black and white.
- 15.
The Beer dataset in arff format is available online at https://gitlab.citius.usc.es/jose.alonso/xai/-/blob/master/BEER3.txt.aux.arff.
- 16.
It is worth noting that GUAJE can run batch scripts from command line with the aim of building models for all the folds at once.
- 17.
The interested reader is kindly invited to revisit Sect. 6.3 and the references there in for further details about how an IFS/GLMP is defined. IFS stands for Interpretable Fuzzy System and GLMP is the acronym of Granular Linguistic Model of a Phenomenon.
- 18.
This Python script is available online along with the rest of files needed to reproduce the use cases illustrated in this chapter (Alonso et al. 2020).
- 19.
The ExpliClas web service:https://demos.citius.usc.es/ExpliClas/.
- 20.
The Waikato Environment for Knowledge Analysis (WEKA): https://www.cs.waikato.ac.nz/ml/weka/.
References
Acampora G, Di Stefano B, Vitiello A (2016) IEEE 1855: The first IEEE standard sponsored by IEEE computational intelligence society. IEEE Comput Intell Mag 11(4):4–7
Alcala-Fdez J, Alonso JM (2016) A survey of fuzzy systems software: taxonomy, current research trends, and prospects. IEEE Transa Fuzzy Syst 24(1):40–56. https://doi.org/10.1109/TFUZZ.2015.2426212
Alcala-Fdez J, Alonso JM, Castiello C, Mencar C, Soto-Hidalgo JM (2019) Py4JFML: a Python wrapper for using the IEEE Std 1855-2016 through JFML. In: IEEE conference on fuzzy systems (FUZZ-IEEE). https://doi.org/10.1109/FUZZ-IEEE.2019.8858811
Alonso JM (2019) From Zadeh’s computing with words towards explainable Artificial Intelligence. In: Fuller R, Giove S, Masulli F (eds) Fuzzy logic and applications. WILF2018. Lecture notes in computer science. Springer Nature Switzerland AG, pp 244–248 (2019). https://doi.org/10.1007/978-3-030-12544-8_21
Alonso JM (2020) Java environment for generating accurate and understandable fuzzy classifiers. https://gitlab.citius.usc.es/jose.alonso/guaje/
Alonso JM, Bugarín A (2019) ExpliClas: automatic generation of explanations in natural language for Weka classifiers. In: IEEE international conference on fuzzy systems (FUZZ-IEEE), pp 1–6 (2019). https://doi.org/10.1109/FUZZ-IEEE.2019.8859018
Alonso JM, Casalino G (2019) Explainable artificial intelligence for human-centric data analysis in virtual learning environments. In: Burgos D, Cimitile M, Ducange P, Pecori R, Picerno P, Raviolo P, Stracke CM (eds) Higher education learning methodologies and technologies online, vol 1091. Springer, pp 125–138 (2019). https://doi.org/10.1007/978-3-030-31284-8_10
Alonso JM, Castiello C, Magdalena L, Mencar C (2020) Supplementary material for the Book entitled “Explainable Fuzzy Systems: Paving the Way from Interpretable Fuzzy Systems to Explainable AI Systems”. https://gitlab.citius.usc.es/jose.alonso/bookexfs/
Alonso JM, Castiello C, Mencar C (2018) A bibliometric analysis of the explainable artificial intelligence research field. In: International conference on information processing and management of uncertainty in knowledge-based systems (IPMU), pp 3–15 (2018). https://doi.org/10.1007/978-3-319-91473-2_1
Alonso JM, Castiello C, Mencar C (2019) The role of interpretable fuzzy systems in designing cognitive cities. In: Designing cognitive cities: linking citizens to computational intelligence to make efficient, sustainable and resilient cities a reality. Springer, pp 131–152 (2019). https://doi.org/10.1007/978-3-030-00317-3_6
Alonso JM, Cordon O, Guillaume S, Magdalena L (2007) Highly interpretable linguistic knowledge bases optimization: genetic tuning versus solis-wetts. Looking for a good interpretability-accuracy trade-off. In: IEEE international conference on fuzzy systems (FUZZ-IEEE), pp 901–906 (2007). https://doi.org/10.1109/FUZZY.2007.4295485
Alonso JM, Ducange P, Pecori R, Vilas R (2020) Building explanations for fuzzy decision trees with the expliclas software. In: IEEE international conference on fuzzy systems (FUZZ-IEEE). https://doi.org/10.1109/FUZZ48607.2020.9177725
Alonso JM, Magdalena L (2011a) Generating understandable and accurate fuzzy rule-based systems in a Java environment. In: Fanelli A, Pedrycz W, Petrosino A (eds) Lecture notes in artificial intelligence. Springer, Berlin, Heidelberg, pp 212–219 (ISSN: 0302-9743), Trani, Bari, Italy. https://doi.org/10.1007/978-3-642-23713-3_27
Alonso JM, Magdalena L (2011b) HILK++: an interpretability-guided fuzzy modeling methodology for learning readable and comprehensible fuzzy rule-based classifiers. Soft Computing 15(10):1959–1980. https://doi.org/10.1007/s00500-010-0628-5
Alonso JM, Magdalena L, Guillaume S (2006) Linguistic knowledge base simplification regarding accuracy and interpretability. Mathware Soft Comput 13(3):203–216
Alonso JM, Magdalena L, Guillaume S (2008) HILK: A new methodology for designing highly interpretable linguistic knowledge bases using the fuzzy logic formalism. Int J Intell Syst 23(7):761–794. https://doi.org/10.1002/int.20288
Alonso JM, Magdalena L, Cordón O (2010) Embedding HILK in a three-objective evolutionary algorithm with the aim of modeling highly interpretable fuzzy rule-based classifiers. In: International workshop on genetic and evolutionary fuzzy systems (GEFS). IEEE, pp 15–20 (2010). https://doi.org/10.1109/GEFS.2010.5454165
Alonso JM, Magdalena L, Guillaume S, Sotelo M, Bergasa L, Ocaña M, Flores R (2007) Knowledge-based intelligent diagnosis of ground robot collision with non detectable obstacles. J Intell Robot Syst 48(4):539–566. https://doi.org/10.1007/s10846-006-9125-6
Alonso JM, Castiello C, Lucarelli M, Mencar C (2012) Modelling interpretable fuzzy rule-based classifiers for medical decision support. In: Magdalena R, Soria E, Guerrero J, Gomez-Sanchis J, Serrano A (eds) Medical applications of intelligent data analysis: research advancements. IGI Global, pp 254–271. https://doi.org/10.4018/978-1-4666-1803-9.ch017
Alonso JM, Castiello C, Magdalena L, Mencar C (2021a) An overview of fuzzy systems. In: Explainable fuzzy systems: paving the way from interpretable fuzzy systems to explainable AI systems. Studies in computational intelligence, Chap. 2. Springer, pp 25–48. https://doi.org/10.1007/978-3-030-71098-9_2
Alonso JM, Castiello C, Magdalena L, Mencar C (2021b) Designing interpretable fuzzy systems. In: Explainable fuzzy systems: paving the way from interpretable fuzzy systems to explainable AI systems. Studies in computational intelligence, Chap. 5. Springer, pp 119–168. https://doi.org/10.1007/978-3-030-71098-9_5
Alonso JM, Castiello C, Magdalena L, Mencar C (2021c) Interpretability constraints and criteria for fuzzy systems. In: Explainable fuzzy systems: paving the way from interpretable fuzzy systems to explainable AI systems. Studies in computational intelligence, Chap. 3. Springer, pp 49–89. https://doi.org/10.1007/978-3-030-71098-9_3
Alonso JM, Castiello C, Magdalena L, Mencar C (2021d) Revisiting indexes for assessing interpretability of fuzzy systems. In: Explainable fuzzy systems: paving the way from interpretable fuzzy systems to explainable AI systems. Studies in computational intelligence, Chap. 4. Springer, pp 91–118. https://doi.org/10.1007/978-3-030-71098-9_4
Alonso JM, Ocaña M, Hernandez N, Herranz F, Llamazares A, Sotelo M, Bergasa L, Magdalena L (2011) Enhanced WiFi localization system based on soft Computing techniques to deal with small-scale variations in wireless sensors. Appl Soft Comput 11(8):4677–4691. https://doi.org/10.1016/j.asoc.2011.07.015
Alonso JM, Pancho DP, Cordón O, Quirin A, Magdalena L (2013) Social network analysis of co-fired fuzzy rules. In: Yager RR, Abbasov AM, Reformat MZ, Shahbazova SN (eds) Soft computing: state of the art theory and novel applications, Studies in fuzziness and soft computing, Chap. 9. Springer, pp 113–128 (2013). https://doi.org/10.1007/978-3-642-34922-5_9
Alonso JM, Ramos-Soto A, Reiter E, Van Deemter K (2017) An exploratory study on the benefits of using natural language for explaining fuzzy rule-based systems. In: IEEE international conference on fuzzy systems (FUZZ-IEEE). https://doi.org/10.1109/FUZZ-IEEE.2017.8015489
Altug S, Chow MY, Trussell H (1999) Heuristic constraints enforcement for training of and rule extraction from a fuzzy/neural architecture. II. Implementation and application. IEEE Trans Fuzzy Syst 7(2):151–159. https://doi.org/10.1109/91.755397
Barrientos F, Sainz G (2011) Interpretable knowledge extraction from emergency call data based on fuzzy unsupervised decision tree. Knowl-Based Syst 25(1):77–87. https://doi.org/10.1016/j.knosys.2011.01.014
Bezdek J (1981) Pattern recognition with fuzzy objective function algorithms. Plenum, New York
Breiman L (2001) Random forest. Mach Learn 45(1):5–32
Carmona C, Gonzalez P, del Jesus M, Navio-Acosta M, Jimenez-Trevino L (2011) Evolutionary fuzzy rule extraction for subgroup discovery in a psychiatric emergency department. Soft computing-a fusion of foundations, methodologies and applications 15(12):2435–2448. https://doi.org/10.1007/s00500-010-0670-3
Castellano G, Castiello C, Fanelli A (2017a) The FISDeT software: application to beer style classification. In: IEEE international conference on fuzzy systems (FUZZ-IEEE), pp 1–6 (2017). https://doi.org/10.1109/FUZZ-IEEE.2017.8015503
Castellano G, Castiello C, Pasquadibisceglie V, Zaza G (2017b) FISDeT: fuzzy inference system development tool. Int J Comput Intell Syst 10:13–22. https://doi.org/10.2991/ijcis.2017.10.1.2
Castiello C, Fanelli AM, Lucarelli M, Mencar C (2019) Interpretable fuzzy partitioning of classified data with variable granularity. Appl Soft Comput 74:567–582. https://doi.org/10.1016/j.asoc.2018.10.040
Castro-Lopez A, Alonso JM (2019) Modeling human perceptions in e-commerce applications: a case study on business-to-consumers websites in the textile and fashion sector. In: Applying fuzzy logic for the digital economy and society. Fuzzy management methods. Springer (2019). https://doi.org/10.1007/978-3-030-03368-2_6
Chen MY (2002) Establishing interpretable fuzzy models from numeric data. In: IEEE world congress on intelligent control and automation, pp 1857–1861 (2002)
Cheong F (2008) A hierarchical fuzzy system with high input dimensions for forecasting foreign exchange rates. Int J Artif Intell Soft Comput 1(1):15–24. https://doi.org/10.1504/IJAISC.2008.021261
Conde-Clemente P, Alonso JM, Trivino G (2017) rLDCP: R package for text generation from data. In: IEEE international conference on fuzzy systems (FUZZ-IEEE), pp 1–6 (2017). https://doi.org/10.1109/FUZZ-IEEE.2017.8015487
Conde-Clemente P, Alonso JM, Trivino G (2018) Toward automatic generation of linguistic advice for saving energy at home. Soft Comput 22(2):345–359. https://doi.org/10.1007/s00500-016-2430-5
Cordón O, Herrera F, Hoffmann F, Magdalena L (2001) Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases. World Scientific
El-Sappagh S, Alonso JM, Ali F, Ali A, Jang JH, KwakK S (2018) An ontology-based interpretable fuzzy decision support system for diabetes diagnosis. IEEE Access 6:37371–37394. https://doi.org/10.1109/ACCESS.2018.2852004
Fernandez-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15(1):3133–3181
Gadaras I, Mikhailov L (2009) An interpretable fuzzy rule-based classification methodology for medical diagnosis. Artif Intell Med 47(1):25–41. https://doi.org/10.1016/j.artmed.2009.05.003
Gatt A, Krahmer E (2018) Survey of the state of the art in natural language generation: core tasks, applications and evaluation. J Artif Intell Res 61:65–170. https://doi.org/10.1613/jair.5477
Gatt A, Reiter E (2009) SimpleNLG: a realisation engine for practical applications. European workshop on natural language generation (ENLG). Greece, Athens, pp 90–93
Ghandar A, Michalewicz Z, Zurbruegg R (2012) Enhancing profitability through interpretability in algorithmic trading with a multiobjective evolutionary fuzzy system. In: Coello C, Cutello V, Deb K, Forrest S, Nicosia G, Pavone M (eds) Parallel problem solving from nature-PPSN XII. Lecture notes in computer science, vol 7492. Springer, Berlin, Heidelberg, pp 42–51 (2012). https://doi.org/10.1007/978-3-642-32964-7_5
Glorennec PY (1999) Algorithmes d’apprentissage pour systemes d’inference floue. Hermes, Paris
Guillaume S, Charnomordic B (2004) Generating an interpretable family of fuzzy partitions from data. IEEE Trans Fuzzy Syst 12(3):324–335. https://doi.org/10.1109/TFUZZ.2004.825979
Guillaume S, Charnomordic B (2010) Interpretable fuzzy inference systems for cooperation of expert knowledge and data in agricultural applications using FisPro. In: IEEE International conference on fuzzy systems (FUZZ-IEEE), pp 2019-2026, Barcelona (2010). https://doi.org/10.1109/FUZZY.2010.5584673
Guillaume S, Charnomordic B (2011) Learning interpretable fuzzy inference systems with FisPro. Inform Sci 181(20):4409–4427. https://doi.org/10.1016/j.ins.2011.03.025
Hartigan JA, Wong MA (1979) A k-means clustering algorithm. Appl Stat 28:100–108
Hühn J, Hüllermeier E (2009) FURIA: an algorithm for unordered fuzzy rule induction. Data Mining Knowl Discov 19(3):293–319. https://doi.org/10.1007/s10618-009-0131-8
Ichihashi H, Shirai T, Nagasaka K, Miyoshi T (1996) Neuro-fuzzy ID3: a method of inducing fuzzy decision trees with linear programming for maximizing entropy and an algebraic method for incremental learning. Fuzzy Sets Syst 81(1):157–167. https://doi.org/10.1016/0165-0114(95)00247-2
Kohonen T (1986) Learning vector quantization for pattern recognition. Helsinki University of Technology, Finland, Technical report
Kohonen T (2001) Self-organizing maps, 3rd edn. Springer, Berlin
Kumar A (2005) Interpretability and mean-square error performance of fuzzy inference systems for data mining. Intell Syst Account Financ Manag 13(4):185–196. https://doi.org/10.1002/isaf.263
Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Mach Stud 7:1–13
MathWorks: Fuzzy logic toolbox. Design and simulate fuzzy logic systems (2019). https://www.mathworks.com/products/fuzzy-logic.html
Mencar C, Castiello C, Cannone R, Fanelli A (2011) Design of fuzzy rule-based classifiers with semantic cointension. Inform Sci 181(20):4361–4377. https://doi.org/10.1016/j.ins.2011.02.014
Mucientes M, Casillas J (2007) Quick design of fuzzy controllers with good interpretability in mobile robotics. IEEE Trans Fuzzy Syst 15(4):636–651. https://doi.org/10.1109/TFUZZ.2006.889889
Pancho DP, Alonso JM, Cordón O, Quirin A, Magdalena L (2013a) FINGRAMS: visual representations of fuzzy rule-based inference for expert analysis of comprehensibility. IEEE Trans Fuzzy Syst 21(6):1133–1149. https://doi.org/10.1109/TFUZZ.2013.2245130
Pancho DP, Alonso JM, Magdalena L (2013b) Quest for interpretability-accuracy trade-off supported by fingrams into the fuzzy modeling tool GUAJE. Int J Comput Intell Syst 6:46–60. https://doi.org/10.1080/18756891.2013.818189
Pulkkinen P, Hytonen J, Koivisto H (2008) Developing a bioaerosol detector using hybrid genetic fuzzy systems. Eng Appl Artif Intell 21(8):1330–1346. https://doi.org/10.1016/j.engappai.2008.01.006
Ramos-Soto A, Janeiro-Gallardo J, Bugarín A (2017) Adapting SimpleNLG to Spanish. In: International conference on natural language generation (INLG). ACL, pp 142–146. https://doi.org/10.18653/v1/W17-3521
Rehse JR, Mehdiyev N, Fettke P (2019) Towards explainable process predictions for industry 4.0 in the DFKI-Smart-Lego-Factory. KI-Künstliche Intelligenz 33(2):181–187 (2019). https://doi.org/10.1007/s13218-019-00586-1
Reiter E, Dale R (2000) Building natural language generation systems. Cambridge University Press
Riid A, Rustern E (2007) Interpretability of fuzzy systems and its application to process control. In: IEEE international conference on fuzzy systems (FUZZ-IEEE), pp 1–6 (2007). https://doi.org/10.1109/FUZZY.2007.4295370
Schoeman W (2016) Why AI is the future of growth. Technical report, Accenture
Solis FJ, Wets RJB (1981) Minimization by random search techniques. Math Oper Res 6(1):19–30
Soto-Hidalgo JM, Alonso JM, Acampora G, Alcala-Fdez J (2018) JFML: A Java library to design fuzzy logic systems according to the IEEE Std 1855–2016. IEEE Access 6:54952–54964. https://doi.org/10.1109/ACCESS.2018.2872777
Trivino G, Sugeno M (2013) Towards linguistic descriptions of phenomena. Int J Approx Reason 54(1):22–34
Troiano L, Rodríguez-Muñiz LJ, Ranilla J, Díaz I (2012) Interpretability of fuzzy association rules as means of discovering threats to privacy. Int J Comput Math 89(3):325–333
Vanbroekhoven E, Adriaenssens V, Debaets B (2007) Interpretability-preserving genetic optimization of linguistic terms in fuzzy models for fuzzy ordered classification: an ecological case study. Int J Approx Reason 44(1):65–90. https://doi.org/10.1016/j.ijar.2006.03.003
Wang LX, Mendel JM (1992) Generating fuzzy rules by learning from examples. IEEE Trans Syst Man Cybern 22(6):1414–1427
Witten IH, Frank E, Hall MA, Pal CJ (2016) Data mining: practical machine learning tools and techniques. Morgan Kaufmann
Zadeh LA (1965) Fuzzy sets. Inform. Control 8:338–353
Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning-I. Inform Sci 8:199–249
Zadeh LA (1999) From computing with numbers to computing with words-from manipulation of measurements to manipulation of perceptions. IEEE Trans Circ Syst I: Fundamental Theory Appl 46(1):105–119
Zadeh LA (2001) A new direction in AI: toward a computational theory of perceptions. Artif Intell Mag 22(1):73–84
Zadeh LA (2011) A note on Z-numbers. Inform Sci 181(14):2923–2932. https://doi.org/10.1016/j.ins.2011.02.022
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Alonso Moral, J.M., Castiello, C., Magdalena, L., Mencar, C. (2021). Design and Validation of an Explainable Fuzzy Beer Style Classifier. In: Explainable Fuzzy Systems. Studies in Computational Intelligence, vol 970. Springer, Cham. https://doi.org/10.1007/978-3-030-71098-9_6
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