Results 41 to 50 of about 524,166 (313)
A Monte Carlo Tree Search Approach to Learning Decision Trees [PDF]
2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018 Comunicació presentada a: 17th IEEE International Conference on Machine Learning and Applications (ICMLA) celebrada del 17 al 20 de 2018 a Orlando, Estats Units. Decision trees (DTs) are a widely used prediction tool, owing to their interpretability. Standard learning methods follow a locally-optimal approach that trades off prediction performance for ...
Nunes, Cecilia +4 more
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Quality Diversity Evolutionary Learning of Decision Trees
Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, 2023 Addressing the need for explainable Machine Learning has emerged as one of the most important research directions in modern Artificial Intelligence (AI). While the current dominant paradigm in the field is based on black-box models, typically in the form of (deep) neural networks, these models lack direct interpretability for human users, i.e., their ...
Andrea Ferigo +2 more
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Frontiers in Oncology, 2023 BackgroundTo investigate the contribution of machine learning decision tree models applied to perfusion and spectroscopy MRI for multiclass classification of lymphomas, glioblastomas, and metastases, and then to bring out the underlying key ...
Rodolphe Vallée +14 more
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Collapsing the Decision Tree: the Concurrent Data Predictor [PDF]
, 2021 A family of concurrent data predictors is derived from the decision tree classifier by removing the limitation of sequentially evaluating attributes. By evaluating attributes concurrently, the decision tree collapses into a flat structure. Experiments indicate improvements of the prediction accuracy.
arxiv +1 more source
METHOD OF BUILDING THE SEMANTIC NETWORK OF DISTRIBUTED SEARCH IN E-LEARNING
Сучасний стан наукових досліджень та технологій в промисловості, 2017 The subject matter of the article is semantic networks of distributed search in e-learning. The goal is to synthesize a decision tree and a stratified semantic network that allows network intelligent agents in the e-learning to construct inference ...
Nina Kuchuk +2 more
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Learning Binary Decision Trees by Argmin Differentiation [PDF]
, 2020 International ...
Zantedeschi, Valentina +2 more
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Comprehensive decision tree models in bioinformatics.
PLoS ONE, 2012 PurposeClassification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of
Gregor Stiglic +3 more
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Multiple Instance Learning with Trainable Soft Decision Tree Ensembles
Algorithms, 2023 A new random forest-based model for solving the Multiple Instance Learning problem under small tabular data, called the Soft Tree Ensemble Multiple Instance Learning, is proposed.
Andrei Konstantinov +2 more
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dtControl: Decision Tree Learning Algorithms for Controller Representation [PDF]
, 2020 Decision tree learning is a popular classification technique most commonly used in machine learning applications. Recent work has shown that decision trees can be used to represent provably-correct controllers concisely. Compared to representations using lookup tables or binary decision diagrams, decision trees are smaller and more explainable.
arxiv +1 more source
On Tackling Explanation Redundancy in Decision Trees [PDF]
J. Artif. Intell. Res. Vol. 75 (2022), 2022 Decision trees (DTs) epitomize the ideal of interpretability of machine learning (ML) models. The interpretability of decision trees motivates explainability approaches by so-called intrinsic interpretability, and it is at the core of recent proposals for applying interpretable ML models in high-risk applications.
arxiv +1 more source