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Found 5 papers, 2 papers with code
Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network
For that, we use more than 100, 000 research papers and build up a knowledge network with more than 64, 000 concept nodes.
Link Prediction of Artificial Intelligence Concepts using Low Computational Power
This paper presents an approach proposed for the Science4cast 2021 competition, organized by the Institute of Advanced Research in Artificial Intelligence, whose main goal was to predict the likelihood of future associations between machine learning concepts in a semantic network.
A New Approach for Interpretability and Reliability in Clinical Risk Prediction: Acute Coronary Syndrome Scenario
In order to achieve the mentioned goals, a three-step methodology was developed: several rules were created by dichotomizing risk factors; such rules were trained with a machine learning classifier to predict the acceptance degree of each rule (the probability that the rule is correct) for each patient; that information was combined and used to compute the risk of mortality and the reliability of such prediction.
Personalized and Reliable Decision Sets: Enhancing Interpretability in Clinical Decision Support Systems
In this study, we present a novel clinical decision support system and discuss its interpretability-related properties.
Improving the compromise between accuracy, interpretability and personalization of rule-based machine learning in medical problems
Some procedures consider a simplification of ETs, using heuristic approaches to select an optimal reduced set of decision rules.
