nach oben

Erschienen in:

2020 | OriginalPaper | Buchkapitel

Evaluating Tree Explanation Methods for Anomaly Reasoning: A Case Study of SHAP TreeExplainer and TreeInterpreter

verfasst von : Pulkit Sharma, Shezan Rohinton Mirzan, Apurva Bhandari, Anish Pimpley, Abhiram Eswaran, Soundar Srinivasan, Liqun Shao

Erschienen in: Advances in Conceptual Modeling

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Understanding predictions made by Machine Learning models is critical in many applications. In this work, we investigate the performance of two methods for explaining tree-based models: ‘Tree Interpreter (TI)’ and ‘SHapley Additive exPlanations TreeExplainer (SHAP-TE)’. Using a case study on detecting anomalies in job runtimes of applications that utilize cloud-computing platforms, we compare these approaches using a variety of metrics, including computation time, significance of attribution value, and explanation accuracy. We find that, although the SHAP-TE offers consistency guarantees over TI, at the cost of increased computation, consistency does not necessarily improve the explanation performance in our case study.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Superimposition: Augmenting Machine Learning Outputs with Conceptual Models for Explainable AI

Nächstes Kapitel The Importance of the Temporal Dimension in Identifying Relevant Genomic Variants: A Case Study

Feature Attribution (FA) is defined as the contribution each independent variable or a “feature” made to the final prediction of a model.

See Sect. 2 for the definition of consistency.

Feature Attribution Method (FAM), referred to as the explanation method that calculates FAs to interpret each prediction generated by a model.

Some of the covariate variables in postgreSQL dataset are continuous, which when grouped reduces the number of data points per cluster.

RBO implementation: https://github.com/changyaochen/rbo.

Dataset can be found at https://groups.cs.umass.edu/kdl/causal-eval-data.

This data is collected in the work by [5].

For eg, consider 2 lists of attribution values \(S_1=[1, 1.1, 1.3]\) and \(S_2=[1, 3, 5]\). The ranking obtained from values in \(S_2\) is more reliable than \(S_1\).

https://github.com/sharmapulkit/TreeInterpretability_AnomalyExplanation.

Caruana, R., Karampatziakis, N., Yessenalina, A.: An empirical evaluation of supervised learning in high dimensions. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008, pp. 96–103 (2008)

Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41, 1–58 (2009)CrossRef

Cuzzocrea, A., Mumolo, E., Cecolin, R.: Runtime anomaly detection in embedded systems by binary tracing and hidden Markov models. In 2015 IEEE 39th Annual Computer Software and Applications Conference, vol. 2, pp. 15–22 (2015)

Duque Anton, S., Sinha, S., Schotten, H.: Anomaly-based intrusion detection in industrial data with SVM and random forests, pp. 1–6 (2019)

Gentzel, A., Garant, D., Jensen, D.: The case for evaluating causal models using interventional measures and empirical data. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, pp. 11722–11732. Curran Associates Inc. (2019)

Kuhn, H.W., Tucker, A.W.: Contributions to the Theory of Games, vol. 2. Princeton University Press, Princeton (1953)MATH

Lipovetsky, S., Conklin, M.: Analysis of regression in game theory approach. Appl. Stochast. Models Bus. Ind. 17, 319–330 (2001)MathSciNetCrossRef

Lundberg, S.M., et al.: From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2(1), 2522–5839 (2020)CrossRef

Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

10.

Peiris, M., Hill, J.H., Thelin, J., Bykov, S., Kliot, G., Konig, C.: PAD: performance anomaly detection in multi-server distributed systems. In: 2014 IEEE 7th International Conference on Cloud Computing, pp. 769–776 (2014)

11.

Primartha, R., Tama, B.A.: Anomaly detection using random forest: a performance revisited. In: 2017 International Conference on Data and Software Engineering (ICoDSE), pp. 1–6 (2017)

12.

Ribeiro, M.T., Singh, S., Guestrin, C.: “why should I trust you?”: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016, pp. 1135–1144 (2016)

13.

Saabas, A.: Treeinterpreter. https://github.com/andosa/treeinterpreter

14.

Shao, L., et al.: Griffon. In: Proceedings of the ACM Symposium on Cloud Computing - SoCC 2019 (2019)

15.

Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. CoRR abs/1704.02685 (2017)

16.

Shrikumar, A., Greenside, P., Shcherbina, A., Kundaje, A.: Not just a black box: learning important features through propagating activation differences. CoRR abs/1605.01713 (2016)

17.

Štrumbelj, E., Kononenko, I.: Explaining prediction models and individual predictions with feature contributions. Knowl. Inf. Syst. 41, 647–665 (2013)CrossRef

18.

Sultani, W., Chen, C., Shah, M.: Real-world anomaly detection in surveillance videos. In: The IEEE Conference on Computer Vision and Pattern Recognition (2018)

19.

Webber, W., Moffat, A., Zobel, J.: A similarity measure for indefinite rankings. ACM Trans. Inf. Syst. 28, 4 (2010)CrossRef

20.

Wulsin, D., Blanco, J., Mani, R., Litt, B.: Semi-supervised anomaly detection for EEG waveforms using deep belief nets. In: 2010 Ninth International Conference on Machine Learning and Applications, pp. 436–441 (2010)

Titel: Evaluating Tree Explanation Methods for Anomaly Reasoning: A Case Study of SHAP TreeExplainer and TreeInterpreter
verfasst von: Pulkit Sharma
Shezan Rohinton Mirzan
Apurva Bhandari
Anish Pimpley
Abhiram Eswaran
Soundar Srinivasan
Liqun Shao
Verlag: Springer International Publishing
Buch: Advances in Conceptual Modeling
Print ISBN: 978-3-030-65846-5

Electronic ISBN: 978-3-030-65847-2

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-65847-2_4

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner