3 papers
cs.LG2025
Monotonic anomaly detection
Oliver Urs Lenz, Matthijs van Leeuwen
Semi-supervised anomaly detection is based on the principle that potential anomalies are those records that look different from normal training data. However, in some cases we are…
cs.LG2025
A unified weighting framework for evaluating nearest neighbour classification
Oliver Urs Lenz, Henri Bollaert, Chris Cornelis
We present the first comprehensive and large-scale evaluation of classical (NN), fuzzy (FNN) and fuzzy rough (FRNN) nearest neighbour classification. We standardise existing propos…
cs.LG2024
No imputation without representation
Oliver Urs Lenz, Daniel Peralta, Chris Cornelis
By filling in missing values in datasets, imputation allows these datasets to be used with algorithms that cannot handle missing values by themselves. However, missing values may i…