no code implementations • 28 Nov 2023 • Oliver Urs Lenz, Henri Bollaert, Chris Cornelis
NN and FRNN perform best with a combination of Samworth rank- and distance weights and scaling by the mean absolute deviation around the median ($r_1$), the standard deviaton ($r_2$) or the interquartile range ($r_{\infty}^*$), while FNN performs best with only Samworth distance-weights and $r_1$- or $r_2$-scaling.
no code implementations • 25 Sep 2023 • Oliver Urs Lenz, Chris Cornelis
Angular Minkowski $p$-distance is a dissimilarity measure that is obtained by replacing Euclidean distance in the definition of cosine dissimilarity with other Minkowski $p$-distances.
no code implementations • 4 Oct 2022 • Oliver Urs Lenz, Daniel Peralta, Chris Cornelis
We propose polar encoding, a representation of categorical and numerical $[0, 1]$-valued attributes with missing values to be used in a classification context.
no code implementations • 28 Jun 2022 • Oliver Urs Lenz, Daniel Peralta, Chris Cornelis
Imputation allows datasets to be used with algorithms that cannot handle missing values by themselves.
no code implementations • 22 Feb 2022 • Adnan Theerens, Oliver Urs Lenz, Chris Cornelis
In classical fuzzy rough sets, the lower and upper approximations are determined using the minimum and maximum operators, respectively.
no code implementations • 4 Feb 2021 • Oliver Urs Lenz, Daniel Peralta, Chris Cornelis
The hyperparameters of SVM and LOF have to be optimised through cross-validation, while NND, LNND and ALP allow an efficient form of leave-one-out validation and the reuse of a single nearest-neighbour query.
no code implementations • 26 Jan 2021 • 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 examples of that class, and no counter-examples.