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Published in 2020 at "Archives of Toxicology"
DOI: 10.1007/s00204-020-02690-w
Abstract: The goal of (eco-) toxicological testing is to experimentally establish a dose or concentration–response and to identify a threshold with a biologically relevant and probably non-random deviation from “normal”. Statistical tests aid this process. Most statistical…
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Keywords:
alternatives statistical;
toxicological bioassays;
eco toxicological;
statistical decision ... See more keywords
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Published in 2025 at "Applied Intelligence"
DOI: 10.1007/s10489-024-05953-6
Abstract: The protection of sensitive data becomes more vital, as data increases in value and potency. Furthermore, the pressure increases from regulators and society on model developers to make their Artificial Intelligence (AI) models non-discriminatory. To…
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Keywords:
decision trees;
sensitive data;
privacy;
fairness estimation ... See more keywords
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Published in 2022 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-021-11744-9
Abstract: With the advancement of technology and the spread of the COVID19 epidemic, learning can no longer only be done through face-to-face teaching. Numerous digital learning materials have appeared in large numbers, changing people’s learning mode.…
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Keywords:
refined kano;
learners needs;
teaching videos;
discover learners ... See more keywords
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Published in 2019 at "International Journal of Computer Vision"
DOI: 10.1007/s11263-019-01237-6
Abstract: Conventional decision trees have a number of favorable properties, including a small computational footprint, interpretability, and the ability to learn from little training data. However, they lack a key quality that has helped fuel the…
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Keywords:
decision;
trees forests;
end learning;
decision trees ... See more keywords
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Published in 2020 at "Journal of Visualization"
DOI: 10.1007/s12650-019-00607-z
Abstract: Abstract Interpreting the decision-making of black boxes in machine learning becomes urgent nowadays due to their lack of transparency. One effective way to interpret these models is to transform them into interpretable surrogate models such…
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Keywords:
decision;
neural networks;
black boxes;
surrogate decision ... See more keywords
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Published in 2020 at "Data Science and Engineering"
DOI: 10.1007/s41019-020-00124-2
Abstract: Fair classification has become an important topic in machine learning research. While most bias mitigation strategies focus on neural networks, we noticed a lack of work on fair classifiers based on decision trees even though…
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Keywords:
decision trees;
tree boosting;
gradient tree;
approach ... See more keywords
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Published in 2017 at "Chemometrics and Intelligent Laboratory Systems"
DOI: 10.1016/j.chemolab.2017.02.004
Abstract: Abstract Decision trees are highly favoured classifiers because of the resemblance of their understandable nature to the branched process of human thinking. But the comprehensible rationality of these trees can be severely affected by the…
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Keywords:
decision;
attribute selection;
selection;
class constraint ... See more keywords
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Published in 2018 at "CIRP Journal of Manufacturing Science and Technology"
DOI: 10.1016/j.cirpj.2017.12.003
Abstract: Abstract This paper aims to (1) compare implementation considerations and challenges for metal and polymer rapid manufacturing (i.e. the use of additive manufacturing technologies for final part production) for mass customisation and (2) derive decision…
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Keywords:
decision;
mass customisation;
decision trees;
rapid manufacturing ... See more keywords
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Published in 2020 at "Cities"
DOI: 10.1016/j.cities.2020.102899
Abstract: Abstract Many studies have explored the relationship between population density and obesity, but there is no consensus, particularly in dense Chinese cities. This study applied gradient boosting decision trees to 2014 national survey data to…
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Keywords:
population density;
gradient boosting;
boosting decision;
decision trees ... See more keywords
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Published in 2021 at "Journal of environmental radioactivity"
DOI: 10.1016/j.jenvrad.2021.106542
Abstract: We present a novel application of machine learning techniques to optimize the design of a radiation detection system. A decision tree-based algorithm is described which greedily optimizes partitioning of energy depositions based on a minimum…
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Keywords:
decision trees;
radioxenon;
minimum detectable;
detectable concentration ... See more keywords
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Published in 2024 at "IEEE Transactions on Dependable and Secure Computing"
DOI: 10.1109/tdsc.2024.3372505
Abstract: Decision tree classifiers are pervasively applied in a wide range of areas, such as healthcare, credit-risk assessment, spam detection, and many more. To ensure effectiveness and efficiency, clients usually choose to adopt classification services that…
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Keywords:
sparse;
decision tree;
secure outsourcing;
decision ... See more keywords