Results 21 to 30 of about 3,746,451 (299)
Inducing decision trees with an ant colony optimization algorithm [PDF]
, 2012 Decision trees have been widely used in data mining and machine learning as a comprehensible knowledge representation. While ant colony optimization (ACO) algorithms have been successfully applied to extract classification rules, decision tree induction ...
Otero, Fernando E.B. +2 more
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DECISION TREES BASED ON MEMRISTOR TECHNOLOGY
Надежность и качество сложных систем, 2022 Background. Despite significant progress in neuroscience recently, understanding of the principles and mechanisms underlying complex brain functions and cognition remains incomplete.
A.Yu. Dorosinskiy +3 more
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Non-Invasive Meningitis Diagnosis Using Decision Trees
IEEE Access, 2020 Meningitis is one of the pandemic diseases that many less developed countries suffer, primarily due to the lack of economic resources to face it. The more severe types of meningitis, Meningococcal Disease, MD, demand immediate medical attention since ...
Viviane M. Lelis +2 more
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Caspian Journal of Neurological Sciences, 2021 Background: Epilepsy is a brain disorder that changes the basin geometry of the oscillation of trajectories in the phase space. Nevertheless, recent studies on epilepsy often used the statistical characteristics of this space to diagnose epileptic ...
Reyhaneh Zarifiyan Irani Nezhad +4 more
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A Bi-criteria optimization model for adjusting the decision tree parameters
Kuwait Journal of Science, 2022 Decision trees play a very important role in knowledge representation because of its simplicity and self-explanatory nature. We study the optimization of the parameters of the decision trees to find a shorter as well as more accurate decision tree ...
Mohammad Azad, Mikhail Moshkov
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, 2015 Model tree induction is a popular method for tackling regression problems requiring interpretable models. Model trees are decision trees with multiple linear regression models at the leaf nodes.
Kramer, Stefan +5 more
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Orthogonal decision trees [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2006 This paper introduces orthogonal decision trees that offer an effective way to construct a redundancy-free, accurate, and meaningful representation of large decision-tree-ensembles often created by popular techniques such as bagging, boosting, random forests, and many distributed and data stream mining algorithms.
Hillol Kargupta +2 more
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Private Boosted Decision Trees via Smooth Re-Weighting
The Journal of Privacy and Confidentiality, 2023 Protecting the privacy of people whose data is used by machine learning algorithms is important. Differential Privacy is the appropriate mathematical framework for formal guarantees of privacy, and boosted decision trees are a popular machine learning ...
Mohammadmahdi Jahanara +4 more
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Simplifying decision trees [PDF]
International Journal of Man-Machine Studies, 1987 Many systems have been developed for constructing decision trees from collections of examples. Although the decision trees generated by these methods are accurate and efficient, they often suffer the disadvantage of excessive complexity and are therefore incomprehensible to experts.
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Decision trees in epidemiological research
Emerging Themes in Epidemiology, 2017 Background In many studies, it is of interest to identify population subgroups that are relatively homogeneous with respect to an outcome. The nature of these subgroups can provide insight into effect mechanisms and suggest targets for tailored ...
Ashwini Venkatasubramaniam +5 more
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