Boosting for Vote Learning in High-Dimensional kNN Classification

Intrinsically high-dimensional data has recently been shown to exhibit substantial hubness in terms of skewne ss of the k-nearest neighbor occurrence frequency distribution. While some points arise as centers of influence and dominate most k-nearest neighbor sets, other points occur very rarely and barely affect the inferred models. Hubness has been shown to be highly detrimental to many learning tasks and several hubnessaware learning methods have recently been proposed. This paper extends the existing fuzzy neighbor occurrence model s in order to enable cost-sensitive learning. We evaluate the ex tended implementations within the context of multi-class boosting, which is used to learn the appropriate neighbor votes during the reweighting iterations. The proposed approach is evaluated o n a series of high-dimensional datasets from various domains. The results demonstrate promising improvements of the propose d approach over the baselines.