Detecting Network Intrusion beyond 1999: Applying Machine Learning Techniques to a Partially Labeled Cybersecurity Dataset. - Jan Klein 004; Sandjai Bhulai; Mark Hoogendoorn; Rob van der Mei; Raymond Hinfelaar (WI, 2018)

Detecting Fraudulent Bookings of Online Travel Agencies with Unsupervised Machine Learning. - Caleb Mensah; Jan Klein 004; Sandjai Bhulai; Mark Hoogendoorn; Rob van der Mei (IEA/AIE, 2019)

Estimating the F1 Score for Learning from Positive and Unlabeled Examples. - Seyed Amin Tabatabaei; Jan Klein 004; Mark Hoogendoorn (LOD (1), 2020)

Plusmine: Dynamic Active Learning with Semi-Supervised Learning for Automatic Classification. - Jan Klein 004; Sandjai Bhulai; Mark Hoogendoorn; Robert D. van der Mei (WI/IAT, 2021)

Jasmine: A New Active Learning Approach to Combat Cybercrime. - Jan Klein 004; Sandjai Bhulai; Mark Hoogendoorn; Rob van der Mei (CoRR, 2021)

The Dutch Draw: Constructing a Universal Baseline for Binary Prediction Models. - Etienne van de Bijl; Jan Klein 004; Joris Pries; Sandjai Bhulai; Mark Hoogendoorn; Rob van der Mei (CoRR, 2022)

The Optimal Input-Independent Baseline for Binary Classification: The Dutch Draw. - Joris Pries; Etienne van de Bijl; Jan Klein 004; Sandjai Bhulai; Rob van der Mei (CoRR, 2023)

The dutch draw: constructing a universal baseline for binary classification problems. - Etienne van de Bijl; Jan Klein 004; Joris Pries; Sandjai Bhulai; Mark Hoogendoorn; Rob van der Mei (J. Appl. Probab., 2025)