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Found 7 papers, 0 papers with code
A unified weighting framework for evaluating nearest neighbour classification
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.
Classifying token frequencies using angular Minkowski $p$-distance
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 imputation without representation
Imputation allows datasets to be used with algorithms that cannot handle missing values by themselves.
Choquet-Based Fuzzy Rough Sets
In classical fuzzy rough sets, the lower and upper approximations are determined using the minimum and maximum operators, respectively.
Optimised one-class classification performance
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.
Average Localised Proximity: A new data descriptor with good default one-class classification performance
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.
