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Decision Tree Learning for Uncertain Clinical Measurements [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2021 Clinical decision requires reasoning in the presence of imperfect data. DTs are a well-known decision support tool, owing to their interpretability, fundamental in safety-critical contexts such as medical diagnosis. However, learning DTs from uncertain data leads to poor generalization, and generating predictions for uncertain data hinders prediction ...
Cecilia Nunes +5 more
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Istanbul Business Research, 2021 Innovation levels and capacities of countries are two very important factors for competitiveness as well as the current Industrial 4.0 Revolution. In this context, capacity and level are relative concepts, with a great need for a common measurement ...
Merve Doğruel, Seniye Ümit Fırat
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On Decision Trees, Influences, and Learning Monotone Decision Trees
, 2004 In this note we prove that a monotone boolean function computable by a decision tree of size s has average sensitivity at most √ log2 s. As a consequence we show that monotone functions are learnable to constant accuracy under the uniform distribution in time polynomial in their decision tree size.
O'Donnell, Ryan, Servedio, Rocco Anthony
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PAC-learning a decision tree with pruning
European Journal of Operational Research, 1996 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Department of Management Information Systems, College of Economics and Business Administration, Kookmin University Chongnung-dong Sungbuk-gu, Seoul South Korea ( host institution ) +2 more
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Learning Optimal Decision Trees with SAT [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 Explanations of machine learning (ML) predictions are of fundamental importance in different settings. Moreover, explanations should be succinct, to enable easy understanding by humans. Decision trees represent an often used approach for developing explainable ML models, motivated by the natural mapping between decision tree paths and rules.
Nina Narodytska +3 more
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Formal Verification of Input-Output Mappings of Tree Ensembles
, 2020 Recent advances in machine learning and artificial intelligence are now being considered in safety-critical autonomous systems where software defects may cause severe harm to humans and the environment. Design organizations in these domains are currently
Nadjm-Tehrani, Simin, Törnblom, John
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Decision Stream: Cultivating Deep Decision Trees
, 2017 Various modifications of decision trees have been extensively used during the past years due to their high efficiency and interpretability. Tree node splitting based on relevant feature selection is a key step of decision tree learning, at the same time ...
Ignatov, Andrey, Ignatov, Dmitry
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Random Prism: An Alternative to Random Forests. [PDF]
, 2011 Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy.
Bramer, Max, Stahl, Frederic
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Malicious URLs Detection Using Decision Tree Classifiers and Majority Voting Technique
Cybernetics and Information Technologies, 2018 Researchers all over the world have provided significant and effective solutions to detect malicious URLs. Still due to the ever changing nature of cyberattacks, there are many open issues.
Patil Dharmaraj R., Patil J. B.
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IEEE Access, 2021 This paper presents the synthesis of multiband frequency selective surfaces (FSSs) using supervised machine learning (ML) with the decision tree (DT) algorithm.
Leidiane C. M. M. Fontoura +4 more
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