Maximum distance minimization for feature weighting

doi:10.1016/j.patrec.2014.10.003

Maximum distance minimization for feature weighting

2015-01-01

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DOI：10.1016/j.patrec.2014.10.003

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We present a new feature weighting method to improve k-Nearest-Neighbor (k-NN) classification. The proposed method minimizes the largest distance between equally labeled data tuples, while retaining a minimum distance between data tuples of different classes, with the goal to group equally labeled data together. It can be implemented as a simple linear program, and in contrast to other feature weighting methods, it does not depend on the initial scaling of the data dimensions. Two versions, a hard and a soft one, are evaluated on real-world datasets from the UCI repository. In particular the soft version compares very well with competing methods. Furthermore, an evaluation is done on challenging gene expression data sets, where the method shows its ability to automatically reduce the dimensionality of the data. (C) 2014 Elsevier B.V. All rights reserved

Keyword:

Feature selection

Feature weighting

Metric learning

k-Nearest-Neighbor

Relief

Large Margin Nearest Neighbor Classification

Maximum distance minimization for feature weighting

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