An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models

Cited by (423)

Items 401–423 of 423

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    Ryma Boumazouza · 2022 · theses.fr (ABES)
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    Adrien Bennetot · 2022 · theses.fr (ABES)
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    Thibault Falque · 2023 · theses.fr (ABES)
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    Astrid Bertrand · 2024 · theses.fr (ABES)
  15. Enhancing ChatGPT with a Decision Support Framework to Resolve Management Decision Scenarios
    Tarmo Koppel, Tarmo Kalvet, Tanel Tammet · 2026 · Lecture notes in business information processing
  16. Generating Mock Skeletons for Lightweight Web-Service Testing with Description Logic Learning
    Thilini Bhagya, Jens Dietrich, Hans W. Guesgen · 2026 · Service Oriented Computing and Applications
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    Shiyu Tang, Jin Rui, Sven Lautenbach, Christina Ludwig, Sukanya Randhawa, Steffen Knoblauch, Alexander Zipf · 2026 · Journal of Transport Geography
  18. From Black Box to Clarity: A Systematic Review of Explainability Methods in Deep Convolutional Neural Networks
    Zina tayari, Mourad Zaied · 2026 · Machine Learning and Knowledge Extraction
  19. An empirical study on the effectiveness of decision trees and decision tables for source-based decision validation
    Victor Olsson, Amin Jalali · 2026 · International Journal of Information Management Data Insights
  20. Risk Assessment in Peacekeeping: Are Visual Sparse Models Transparent?
    Niklas Keller, Uwe Czienskowski, Harald Schaub, Konstantinos V. Katsikopoulos · 2026 · Risk Analysis
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  22. Explainable AI for Scientific Applications: Making AI Models Interpretable in Healthcare, Climate Science, and Engineering
    Mohammad Mahmudur Rahman, Md Shakawat Hossain Rabbi, Md Zubair Ahmad, MD Amir Hossain Fahad, Mohammad Abdul Karim, S M Jobayer Al Amin · 2026 · Technology and Innovation Conference (TIC)
  23. How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation
    Narayanan, Menaka, Chen, Emily, He, Jeffrey, Kim, Been, Gershman, Sam, Doshi-Velez, Finale · 2018 · arXiv