Balancing large margin nearest neighbours for imbalanced data

Liquid-liquid equilibrium data prediction using large margin nearest neighbor

Guanidine hydrochloride has been widely used in the initial recovery steps of active protein from the inclusion bodies in aqueous two-phase system (ATPS). The knowledge of the guanidine hydrochloride effects on the liquid-liquid equilibrium (LLE) phase diagram behavior is still inadequate and no comprehensive theory exists for the prediction of the experimental trends. Therefore the effect the ...

Liquid-Liquid Equilibrium Data Prediction using Large Margin Nearest Neighbor

1Faculty of Chemical Engineering, Babol University of Technology, PO Box, 484, Babol, Iran 2Department of Chemical Engineering, Faculty of Chemical Engineering and Environmental Protection,“Gheorghe Asachi” Technical University of Iaşi, Str. Prof. Dr. Doc. DimitrieMangeron, nr. 73, 700050, Iaşi, Romania 3Department of Computer Science and Engineering, Faculty of Automatic Control and Computer E...

Gradient Boosted Large Margin Nearest Neighbors

A fundamental question of machine learning is how to compare examples. If an algorithm could perfectly determine whether two examples were semantically similar or dissimilar, most subsequent machine learning tasks would become trivial (i.e, the 1-nearest-neighbor classifier will achieve perfect results). A common choice for a dissimilarity measurement is an uninformed norm, like the Euclidean d...

Smoothing Using Nearest Neighbours

Mixtures of Large Margin Nearest Neighbor Classifiers

The accuracy of the k-nearest neighbor algorithm depends on the distance function used to measure similarity between instances. Methods have been proposed in the literature to learn a good distance function from a labelled training set. One such method is the large margin nearest neighbor classifier that learns a global Mahalanobis distance. We propose a mixture of such classifiers where a gati...