Explainable deep learning in healthcare: A methodological survey from an attribution view
The increasing availability of large collections of electronic health record (EHR) data and
unprecedented technical advances in deep learning (DL) have sparked a surge of research …
unprecedented technical advances in deep learning (DL) have sparked a surge of research …
Rule extraction algorithm for deep neural networks: A review
T Hailesilassie - arXiv preprint arXiv:1610.05267, 2016 - arxiv.org
Despite the highest classification accuracy in wide varieties of application areas, artificial
neural network has one disadvantage. The way this Network comes to a decision is not …
neural network has one disadvantage. The way this Network comes to a decision is not …
[HTML][HTML] Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …
applications, but the outcomes of many AI models are challenging to comprehend and trust …
Deepred–rule extraction from deep neural networks
JR Zilke, E Loza Mencía, F Janssen - … , DS 2016, Bari, Italy, October 19–21 …, 2016 - Springer
Neural network classifiers are known to be able to learn very accurate models. In the recent
past, researchers have even been able to train neural networks with multiple hidden layers …
past, researchers have even been able to train neural networks with multiple hidden layers …
Extract interpretability-accuracy balanced rules from artificial neural networks: A review
Artificial neural networks (ANN) have been widely used and have achieved remarkable
achievements. However, neural networks with high accuracy and good performance often …
achievements. However, neural networks with high accuracy and good performance often …
DeNNeS: deep embedded neural network expert system for detecting cyber attacks
S Mahdavifar, AA Ghorbani - Neural Computing and Applications, 2020 - Springer
With the advances in computing powers and increasing volumes of data, deep learning's
emergence has helped revitalize artificial intelligence research. There is a growing trend of …
emergence has helped revitalize artificial intelligence research. There is a growing trend of …
Review of research in the field of developing methods to extract rules from artificial neural networks
AN Averkin, SA Yarushev - Journal of Computer and Systems Sciences …, 2021 - Springer
A large-scale review and analysis of the existing methods and approaches to extract rules
from artificial neural networks, including deep learning neural networks, is carried out. A …
from artificial neural networks, including deep learning neural networks, is carried out. A …
Knowledge augmented machine learning with applications in autonomous driving: A survey
The availability of representative datasets is an essential prerequisite for many successful
artificial intelligence and machine learning models. However, in real life applications these …
artificial intelligence and machine learning models. However, in real life applications these …
A survey of neural trees
Neural networks (NNs) and decision trees (DTs) are both popular models of machine
learning, yet coming with mutually exclusive advantages and limitations. To bring the best of …
learning, yet coming with mutually exclusive advantages and limitations. To bring the best of …
On the evaluation of the symbolic knowledge extracted from black boxes
F Sabbatini, R Calegari - AI and Ethics, 2024 - Springer
As opaque decision systems are being increasingly adopted in almost any application field,
issues about their lack of transparency and human readability are a concrete concern for …
issues about their lack of transparency and human readability are a concrete concern for …