Results 41 to 50 of about 4,693,052 (300)
Privacy-Preserving Gradient Boosting Decision Trees [PDF]
AAAI Conference on Artificial Intelligence, 2019 The Gradient Boosting Decision Tree (GBDT) is a popular machine learning model for various tasks in recent years. In this paper, we study how to improve model accuracy of GBDT while preserving the strong guarantee of differential privacy. Sensitivity and
Q. Li +3 more
semanticscholar +1 more source
International Journal of Prognostics and Health Management, 2013 This study presents the methods employed by a team from the department of Mechatronics and Dynamics at the University of Paderborn, Germany for the 2013 PHM data challenge.
James K. Kimotho +3 more
doaj +1 more source
Comparative Analysis of Deterministic and Nondeterministic Decision Trees for Decision Tables from Closed Classes [PDF]
arXiv, 2023 In this paper, we consider classes of decision tables with many-valued decisions closed under operations of removal of columns, changing of decisions, permutation of columns, and duplication of columns. We study relationships among three parameters of these tables: the complexity of a decision table (if we consider the depth of decision trees, then the
arxiv
KnowledgeKnowledge comes in various forms: scientific, artistic, legal, and many others. For most non-computer scientists, it is far easier to express their knowledge in text than in programming code.
Joseph Vidal +5 more
doaj +1 more source
Data-Driven EEG Band Discovery with Decision Trees
Sensors, 2022 Electroencephalography (EEG) is a brain imaging technique in which electrodes are placed on the scalp. EEG signals are commonly decomposed into frequency bands called delta, theta, alpha, and beta.
Shawhin Talebi +4 more
doaj +1 more source
Learning Optimal Decision Trees with SAT
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.
Nina Narodytska +3 more
semanticscholar +1 more source
EntropyIn this paper, we consider classes of decision tables with many-valued decisions closed under operations of the removal of columns, the changing of decisions, the permutation of columns, and the duplication of columns.
Azimkhon Ostonov, Mikhail Moshkov
doaj +1 more source
A Local Approach to Studying the Time and Space Complexity of Deterministic and Nondeterministic Decision Trees [PDF]
arXiv, 2023 In this paper, we study arbitrary infinite binary information systems each of which consists of an infinite set called universe and an infinite set of two-valued functions (attributes) defined on the universe. We consider the notion of a problem over information system, which is described by a finite number of attributes and a mapping associating a ...
arxiv
Machine Learning Methods with Decision Forests for Parkinson’s Detection
Applied Sciences, 2021 Biomedical engineers prefer decision forests over traditional decision trees to design state-of-the-art Parkinson’s Detection Systems (PDS) on massive acoustic signal data.
Moumita Pramanik +4 more
doaj +1 more source