17 citations · 44 across the 9 of their papers we have counts for
9 papers
Monotonic anomaly detection
Oliver Urs Lenz, Matthijs van Leeuwen
Semi-supervised anomaly detection is based on the principle that any record that looks different from normal training data is a potential anomaly. However, in some cases we are spe…
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…
Classifying token frequencies using angular Minkowski -distance
Oliver Urs Lenz, Chris Cornelis
Angular Minkowski -distance is a dissimilarity measure that is obtained by replacing Euclidean distance in the definition of cosine dissimilarity with other Minkowski -distan…
Polar Encoding: A Simple Baseline Approach for Classification with Missing Values
Oliver Urs Lenz, Daniel Peralta, Chris Cornelis
We propose polar encoding, a representation of categorical and numerical -valued attributes with missing values to be used in a classification context. We argue that this is…
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…
Choquet-Based Fuzzy Rough Sets
Adnan Theerens, Oliver Urs Lenz, Chris Cornelis
Fuzzy rough set theory can be used as a tool for dealing with inconsistent data when there is a gradual notion of indiscernibility between objects. It does this by providing lower…
Optimised one-class classification performance
Oliver Urs Lenz, Daniel Peralta, Chris Cornelis
We provide a thorough treatment of one-class classification with hyperparameter optimisation for five data descriptors: Support Vector Machine (SVM), Nearest Neighbour Distance (NN…
Average Localised Proximity: A new data descriptor with good default one-class classification performance
Oliver Urs Lenz, Daniel Peralta, Chris Cornelis
One-class classification is a challenging subfield of machine learning in which so-called data descriptors are used to predict membership of a class based solely on positive exampl…
A test for monitoring under- and overtreatment in Dutch hospitals
Oliver Urs Lenz, Daniel L Oberski
Over- and undertreatment harm patients and society and confound other healthcare quality measures. Despite a growing body of research covering specific conditions, we lack tools to…