4 papers · 1 filter
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…
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…