89 citations · 162 across the 21 of their papers we have counts for
8 papers · 1 filter
Improving Label Error Detection and Elimination with Uncertainty Quantification
Johannes Jakubik, Michael Vössing, Manil Maskey +2
Identifying and handling label errors can significantly enhance the accuracy of supervised machine learning models. Recent approaches for identifying label errors demonstrate that…
Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts
Johannes Jakubik, Daniel Weber, Patrick Hemmer +2
Information systems increasingly leverage artificial intelligence (AI) and machine learning (ML) to generate value from vast amounts of data. However, ML models are imperfect and c…
Enabling Inter-organizational Analytics in Business Networks Through Meta Machine Learning
Robin Hirt, Niklas Kühl, Dominik Martin +1
Successful analytics solutions that provide valuable insights often hinge on the connection of various data sources. While it is often feasible to generate larger data pools within…
Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Series
Max Schemmer, Joshua Holstein, Niklas Bauer +2
Detecting rare events is essential in various fields, e.g., in cyber security or maintenance. Often, human experts are supported by anomaly detection systems as continuously monito…
Switching Scheme: A Novel Approach for Handling Incremental Concept Drift in Real-World Data Sets
Lucas Baier, Vincent Kellner, Niklas Kühl +1
Machine learning models nowadays play a crucial role for many applications in business and industry. However, models only start adding value as soon as they are deployed into produ…
How to Learn from Others: Transfer Machine Learning with Additive Regression Models to Improve Sales Forecasting
Robin Hirt, Niklas Kühl, Yusuf Peker +1
In a variety of business situations, the introduction or improvement of machine learning approaches is impaired as these cannot draw on existing analytical models. However, in many…