Results 181 to 190 of about 159,034 (226)

Interpretable machine learning with reject option

at - Automatisierungstechnik, 2018
Abstract Classification by means of machine learning models constitutes one relevant technology in process automation and predictive maintenance. However, common techniques such as deep networks or random forests suffer from their black box characteristics and possible adversarial examples. In this contribution, we give an overview about
Brinkrolf, Johannes, Hammer, Barbara
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Interpretable machine learning

2020
Machine learning (ML, a type of artificial intelligence) is increasingly being used to support decision making in a variety of applications including recruitment and clinical diagnoses. While ML has many advantages, there are concerns that in some cases it may not be possible to explain completely how its outputs have been produced. This POSTnote gives
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Predicting Alzheimer’s Disease with Interpretable Machine Learning

Dementia and Geriatric Cognitive Disorders, 2023
Introduction: This study aimed to develop novel machine learning models for predicting Alzheimer’s disease (AD) and identify key factors for targeted prevention. Methods: We included 1,219, 863, and 482 participants aged 60+ years with only sociodemographic, both sociodemographic and self-reported health, both the former two and blood biomarkers ...
Maoni Jia, Yafei Wu, Chaoyi Xiang, Ya Fang   +3 more
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Interpretable Machine Learning in Healthcare

Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, 2018
This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning models are needed in healthcare and how they should be deployed. Additionally, we explore the landscape of
Muhammad Aurangzeb Ahmad, Ankur Teredesai, Carly Eckert   +2 more
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Interpretable machine learning in bioinformatics

Methods, 2020
Young-Rae, Cho, Mingon, Kang
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Interpretable machine learning assessment

Neurocomputing, 2023
Henry Han, Yi Wu, Jiacun Wang, Ashley Han   +3 more
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Algorithms for interpretable machine learning

Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014
It is extremely important in many application domains to have transparency in predictive modeling. Domain experts do not tend to prefer "black box" predictive model models. They would like to understand how predictions are made, and possibly, prefer models that emulate the way a human expert might make a decision, with a few important variables, and a ...
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Interpretable machine learning for building energy management: A state-of-the-art review

Advances in Applied Energy, 2023
zhe chen, Fu Xiao, Fangzhou Guo
exaly