Abstract
Understanding black-box Machine Learning methods on multidimensional data is a key challenge in Machine Learning. While many powerful Machine Learning methods already exist, these methods are often unexplainable or perform poorly on complex data. This paper proposes Visual Knowledge Discovery approaches based on several forms of lossless General Line Coordinates. These are an expansion of the previously introduced General Line Coordinates Linear and Dynamic Scaffolding Coordinates to produce, explain, and visualize non-linear classifiers with explanation rules. To ensure these non-linear models and rules are accurate, General Line Coordinates Linear also developed new interactive Visual Knowledge Discovery algorithms for finding worst-case validation splits. These expansions are General Line Coordinates non-linear, interactive rules linear, hyperblock rules linear, and worst-case linear. Experiments across multiple benchmark datasets show that this Visual Knowledge Discovery method can compete with other visual and computational Machine Learning algorithms while improving both interpretability and accuracy in linear and non-linear classifications. Major benefits from these expansions consist of the ability to build accurate and highly interpretable models and rules from hyperblocks, the ability to analyze interpretability weaknesses in a model, and the input of expert knowledge through interactive and human-guided visual knowledge discovery methods.
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References
Kovalerchuk B (2018) Visual knowledge discovery and machine learning. Springer
Kovalerchuk B, Dovhalets D (2017) Constructing Interactive Visual classification, clustering, and dimension reduction models for N-D Data. Informatics 4(3):1–27. https://doi.org/10.3390/informatics4030023
Kovalerchuk B, Ahmad MA, Teredesai A (2021) Survey of explainable machine learning with visual and granular methods beyond quasi-explanations. Interpretable artificial intelligence: a perspective of granular computing. Springer, pp 217–67. https://arxiv.org/pdf/2009.10221
Kovalerchuk B, Nazemi K, Andonie R, Datia N, Bannissi E (eds) (2022) Integrating artificial intelligence and visualization for visual knowledge discovery. Springer
Ribeiro MT, Singh S, Guestrin C (2016) Why should I trust you?. Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD. pp 1135–1144
Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. Advances in neural information processing systems, pp 1–10. https://proceedings.neurips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf
Molnar C, Casalicchio G, Bischl B (2020) Interpretable machine learning–a brief history, state-of-the-art and challenges. In: Joint European conference on machine learning and knowledge discovery in databases. Springer, Cham, pp 417–431
Freitas AA (2014) Comprehensible classification models: a position paper. ACM SIGKDD explorations newsletter 15(1):1, 10
Hall P, Phan W, Ambati S (2017) Ideas on interpreting machine learning. https://www.oreilly.com/radar/ideas-on-interpreting-machine-learning/
Molnar C (2020) Interpretable machine learning. https://christophm.github.io/interpretable-ml-book/
Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, Katz R, Himmelfarb J, Bansal N, Lee SI (2020) From local explanations to global understanding with explainable AI for trees. Nat Mach Intell 2(1):56–67. https://doi.org/10.1038/s42256-019-0138-9
Holzinger A, Saranti A, Molnar C, Biecek P, Samek W (2022) Explainable AI methods-a brief overview. In: International workshop on extending explainable AI beyond deep models and classifiers. Springer, pp 13–38
C Recaido B (2022) Kovalerchuk, interpretable machine learning for self-service high-risk decision-making. In: 26th international conference information visualization. IEEE, pp 322–329. arXiv:2205.04032
Criticisms of econometrics (2022) https://en.wikipedia.org/wiki/Criticisms_of_econometrics
Moraffah R, Karami M, Guo R, Raglin A, Liu H (2020) Causal interpretability for machine learning-problems, methods and evaluation. ACM SIGKDD Explorations Newsl 22(1):18–33
Li J, Cheng K, Wang S, Morstatter F, Trevino RP, Tang J, Liu H (2018) Feature selection: a data perspective. ACM Comput Surv (CSUR) 50(6):94
Miller GA (1956) The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev 63(2):81–97. https://doi.org/10.1037/h0043158
Lasso G, Khan S, Allen SA, Mariano M, Florez C, Orner EP, Quiroz JA, Quevedo G, Massimi A, Hegde A, Wirchnianski AS, Bortzrd Malonis RH, Georgiev GI, Tong K, Herrera NG, Morano NC (2022) Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes. Plos Comput Biol 19(3)
Doshi-Velez F, Kim B (2017) Towards a rigorous science of interpretable machine learning. https://arxiv.org/abs/1702.08608
Xanthopoulos I, Tsamardinos I, Christophides V, Simon E, Salinger A (2020) Putting the human back in the AutoML loop. In: CEUR workshop proceedings. http://ceur-ws.org/Vol-2578/ETMLP5.pdf
Kovalerchuk B, Schwing J (eds) (2005) Visual and spatial analysis: advances in visual data mining, reasoning and problem solving. Springer
Ribeiro MT, Singh S, Guestrin C (2018) Anchors: high-precision model-agnostic explanations. Proc AAAI Conf Artif Intell 32(1)
Guidotti R, Monreale A, Ruggieri S, Pedreschi D, Turini F, Giannotti F (2018) Local rule-based explanations of black box decision systems. arXiv preprint arXiv:1805.10820.
Marques-Silva J, Ignatiev A (2022) Delivering trustworthy AI through formal XAI. Proc AAAI 3806–3814. https://www.aaai.org/AAAI22Papers/SMT-00448-Marques-SilvaJ.pdf
Kovalerchuk B, Hayes D (2021) Discovering interpretable machine learning models in parallel coordinates. In: 2021 25th International conference information visualisation (IV). IEEE, pp 181–188. arXiv:2106.07474
Mitchell T (1997) Machine learning, McGraw-hill
Muggleton S (ed) (1992) Inductive logic programming. Morgan Kaufmann
Džeroski S (2009) Relational data mining. In: Data mining and knowledge discovery handbook. Springer, Boston, MA, pp 887–911
Kovalerchuk B, Vityaev E (2000) Data mining in finance: advances in relational and hybrid methods. Kluwer
Kernel method. Wikipedia. https://en.wikipedia.org/wiki/Kernel_method
Support Vector Machines. scikit. (n.d.). https://scikit-learn.org/stable/modules/svm. Accessed 20 Feb 2023
Kovalerchuk B, Neuhaus N (2018) Toward efficient automation of interpretable machine learning. In: 2018 IEEE international conference on big data. Seattle, IEEE, pp 4933–4940. 978-1-5386-5035-6/18
Kovalerchuk B (2020) Enhancement of cross validation using hybrid visual and analytical means with Shannon function. In: Beyond traditional probabilistic data processing techniques: interval, fuzzy etc. methods and their applications. Springer, Cham, pp 517–543
Wagle SN, Kovalerchuk B (2022) Self-service data classification using interactive visualization and interpretable machine learning. In: Integrating artificial intelligence and visualization for visual knowledge discovery. Springer, Cham, pp 101–139
GitHub: https://github.com/CWU-VKD-LAB, DV2.0, DSCVis
Acknowledgements
We are thankful for the past experiments and work performed by Morgan Leblanc, Daniel Van Houten, Tyler Swan, Fawziah Alkharnda, and Stephan Adams on previous versions of our software system.
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Appendix
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HBs created with GLC-HBRL with Wisconsin Breast Cancer Dataset. a HBs visualized in parallel coordinates, b HBs visualized in GLC-L, c Analytics for each HB
HBs created with GLC-HBRL with Ionosphere Dataset. a HBs visualized in parallel coordinates. b HBs visualized in GLC-L. c Analytics for each HB
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Huber, L., Kovalerchuk, B., Recaido, C. (2024). Visual Knowledge Discovery with General Line Coordinates. In: Kovalerchuk, B., Nazemi, K., Andonie, R., Datia, N., Bannissi, E. (eds) Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery. Studies in Computational Intelligence, vol 1126. Springer, Cham. https://doi.org/10.1007/978-3-031-46549-9_5
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