Skip to main content
Springer Nature Link
Log in

Towards ML Explainability with Rough Sets, Clustering, and Dimensionality Reduction

  • Conference paper
  • First Online:
Rough Sets (IJCRS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14481))

Included in the following conference series:

  • 474 Accesses

Abstract

This study discusses some essential problems of explainable machine learning applications in the FMCG market. The solution combines several machine learning techniques, including clustering, dimensionality reduction, rough set reducts, and rule-based explanations. We propose a novel approach to improve human-computer interaction with the XAI prototype method by generating human-readable cluster descriptions, emphasizing each cluster’s most discernible characteristics. To evaluate our method, we refer to the challenging task of demand prediction. The results confirmed that we could achieve five times better work performance without losing quality.

Research co-funded by Polish National Centre for Research and Development (NCBiR) grant no. POIR.01.01.01-00-0963/19-00 and by Polish National Science Centre (NCN) grant no. 2018/31/N/ST6/00610.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on Explainable Artificial Intelligence (XAI). IEEE Access 6, 52138–52160 (2018). https://doi.org/10.1109/ACCESS.2018.2870052

    Article  Google Scholar 

  2. Barredo Arrieta, A., et al.: Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020). https://doi.org/10.1016/j.inffus.2019.12.012

    Article  Google Scholar 

  3. Baryannis, G., Dani, S., Antoniou, G.: Predicting supply chain risks using machine learning: the trade-off between performance and interpretability. Futur. Gener. Comput. Syst. 101, 993–1004 (2019). https://doi.org/10.1016/j.future.2019.07.059

    Article  Google Scholar 

  4. Dutta, S., Skowron, A.: Concepts approximation through dialogue with user. In: Mihálydeák, T., et al. (eds.) IJCRS 2019. LNCS (LNAI), vol. 11499, pp. 295–311. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22815-6_23

    Chapter  Google Scholar 

  5. Fisher, A., Rudin, C., Dominici, F.: All models are wrong, but many are useful: learning a variable’s importance by studying an entire class of prediction models simultaneously. J. Mach. Learn. Res. 20, 177:1–177:81 (2019)

    Google Scholar 

  6. Frey, C.B., Osborne, M.A.: The future of employment: how susceptible are jobs to computerisation? Technol. Forecast. Soc. Chang. 114, 254–280 (2017). https://doi.org/10.1016/j.techfore.2016.08.019

    Article  Google Scholar 

  7. Goy, S., Coors, V., Finn, D.: Grouping techniques for building stock analysis: a comparative case study. Energy Build. 236, 110754 (2021). https://doi.org/10.1016/j.enbuild.2021.110754

    Article  Google Scholar 

  8. Grzegorowski, M.: Selected aspects of interactive feature extraction. In: Peters, J.F., Skowron, A., Bhaumik, R.N., Ramanna, S. (eds.) Transactions on Rough Sets XXIII. LNCS, vol. 13610, pp. 121–287. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-662-66544-2_8

    Chapter  Google Scholar 

  9. Grzegorowski, M., Litwin, J., Wnuk, M., Pabis, M., Marcinowski, L.: Survival-based feature extraction - application in supply management for dispersed vending machines. IEEE Trans. Ind. Inform. 19(3), 3331–3340 (2023). https://doi.org/10.1109/TII.2022.3178547

    Article  Google Scholar 

  10. Grzegorowski, M., Ślȩzak, D.: On resilient feature selection: computational foundations of r-C-reducts. Inf. Sci. 499, 25–44 (2019). https://doi.org/10.1016/j.ins.2019.05.041

    Article  MathSciNet  Google Scholar 

  11. Guo, X., Lin, H., Wu, Y., Peng, M.: A new data clustering strategy for enhancing mutual privacy in healthcare IoT systems. Futur. Gener. Comput. Syst. 113, 407–417 (2020). https://doi.org/10.1016/j.future.2020.07.023

    Article  Google Scholar 

  12. Heide, N.F., Muller, E., Petereit, J., Heizmann, M.: \(X^3\)SEG: model-agnostic explanations for the semantic segmentation of 3D point clouds with prototypes and criticism. In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 3687–3691 (2021). https://doi.org/10.1109/ICIP42928.2021.9506624

  13. Janusz, A., Ślęzak, D.: Computation of approximate reducts with dynamically adjusted approximation threshold. In: Esposito, F., Pivert, O., Hacid, M.-S., Raś, Z.W., Ferilli, S. (eds.) ISMIS 2015. LNCS (LNAI), vol. 9384, pp. 19–28. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25252-0_3

    Chapter  Google Scholar 

  14. Kannout, E., Grodzki, M., Grzegorowski, M.: Towards addressing item cold-start problem in collaborative filtering by embedding agglomerative clustering and FP-growth into the recommendation system, vol. 2023 OnLine-First (2023). https://doi.org/10.2298/CSIS221116052K

  15. Khan, I.A., et al.: XSRU-IoMT: explainable simple recurrent units for threat detection in internet of medical things networks. Futur. Gener. Comput. Syst. 127, 181–193 (2022). https://doi.org/10.1016/j.future.2021.09.010

    Article  Google Scholar 

  16. Khan, S.A., Naim, I., Kusi-Sarpong, S., Gupta, H., Idrisi, A.R.: A knowledge-based experts’ system for evaluation of digital supply chain readiness. Knowl.-Based Syst. 228, 107262 (2021). https://doi.org/10.1016/j.knosys.2021.107262

    Article  Google Scholar 

  17. Kobak, D., Berens, P.: The art of using t-SNE for single-cell transcriptomics. Nat. Commun. 10, 5416 (2019). https://doi.org/10.1038/s41467-019-13056-x

    Article  Google Scholar 

  18. McInnes, L., Healy, J., Melville, J.: UMAP: uniform manifold approximation and projection for dimension reduction (2018). https://doi.org/10.48550/arXiv.1802.03426

  19. Nguyen, H.S., Jankowski, A., Peters, J.F., Skowron, A., Stepaniuk, J., Szczuka, M.: Discovery of process models from data and domain knowledge: a rough-granular approach. IGI Glob. (2010). https://doi.org/10.4018/978-1-60566-324-1.ch002

    Article  Google Scholar 

  20. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177(1), 3–27 (2007). https://doi.org/10.1016/j.ins.2006.06.003

    Article  MathSciNet  Google Scholar 

  21. Pawlak, Z.: Rough sets. Int. J. Comput. Inform. Sci. 11, 341–356 (1982). https://doi.org/10.1007/BF01001956

    Article  Google Scholar 

  22. Penta, A., Pal, A.: What is this cluster about? Explaining textual clusters by extracting relevant keywords. Knowl.-Based Syst. 229, 107342 (2021). https://doi.org/10.1016/j.knosys.2021.107342

    Article  Google Scholar 

  23. Riza, L.S., et al.: Implementing algorithms of rough set theory and fuzzy rough set theory in the R package ‘RoughSets’. Inf. Sci. 287, 68–89 (2014). https://doi.org/10.1016/j.ins.2014.07.029

    Article  Google Scholar 

  24. Rudin, C.: Please stop explaining black box models for high stakes decisions. CoRR abs/1811.10154 (2018). arxiv.org/abs/1811.10154

  25. Stawicki, S., Ślȩzak, D., Janusz, A., Widz, S.: Decision bireducts and decision reducts - a comparison. Int. J. Approx. Reason. 84, 75–109 (2017). https://doi.org/10.1016/j.ijar.2017.02.007

    Article  MathSciNet  Google Scholar 

  26. Suraj, Z.: Discovering concurrent process models in data: a rough set approach. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds.) RSFDGrC 2009. LNCS (LNAI), vol. 5908, pp. 12–19. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-10646-0_2

    Chapter  Google Scholar 

  27. Tarallo, E., Akabane, G.K., Shimabukuro, C.I., Mello, J., Amancio, D.: Machine learning in predicting demand for fast-moving consumer goods: an exploratory research. IFAC-PapersOnLine 52(13), 737–742 (2019). https://doi.org/10.1016/j.ifacol.2019.11.203. 9th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2019

  28. Xu, X., Lu, Y., Vogel-Heuser, B., Wang, L.: Industry 4.0 and Industry 5.0 - inception, conception and perception. J. Manuf. Syst. 61, 530–535 (2021). https://doi.org/10.1016/j.jmsy.2021.10.006

    Article  Google Scholar 

  29. Zhang, C.X., Zhang, J.S., Yin, Q.Y.: A ranking-based strategy to prune variable selection ensembles. Knowl.-Based Syst. 125, 13–25 (2017). https://doi.org/10.1016/j.knosys.2017.03.031

    Article  Google Scholar 

  30. Zong, W., Chow, Y., Susilo, W.: Interactive three-dimensional visualization of network intrusion detection data for machine learning. Future Gener. Comput. Syst. 102, 292–306 (2020). https://doi.org/10.1016/j.future.2019.07.045

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Informatics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland

    Marek Grzegorowski & Andrzej Janusz

  2. QED Software, Mazowiecka 11/49, 00-052, Warsaw, Poland

    Andrzej Janusz

  3. FitFood, Solskiego 11/28, 31-216, Kraków, Poland

    Grzegorz Śliwa & Łukasz Marcinowski

  4. Systems Research Institute, Polish Academy of Sciences, Nawelska 6, 01-447, Warszawa, Poland

    Andrzej Skowron

Authors
  1. Marek Grzegorowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrzej Janusz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Grzegorz Śliwa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Łukasz Marcinowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andrzej Skowron
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marek Grzegorowski .

Editor information

Editors and Affiliations

  1. IRCCS Istituto Ortopedico Galeazzi, Milano, Italy

    Andrea Campagner

  2. Ghent University, Ghent, Belgium

    Oliver Urs Lenz

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Shuyin Xia

  4. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  5. AGH University of Science and Technology, Kraków, Poland

    Jarosław Wąs

  6. University of Regina, Regina, SK, Canada

    JingTao Yao

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Grzegorowski, M., Janusz, A., Śliwa, G., Marcinowski, Ł., Skowron, A. (2023). Towards ML Explainability with Rough Sets, Clustering, and Dimensionality Reduction. In: Campagner, A., Urs Lenz, O., Xia, S., Ślęzak, D., Wąs, J., Yao, J. (eds) Rough Sets. IJCRS 2023. Lecture Notes in Computer Science(), vol 14481. Springer, Cham. https://doi.org/10.1007/978-3-031-50959-9_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-50959-9_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50958-2

  • Online ISBN: 978-3-031-50959-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics