NDLI: Boosting for Vote Learning in High-Dimensional kNN Classification

Content Provider	IEEE Xplore Digital Library
Author	Tomaev, Nenad
Copyright Year	2014
Abstract	Intrinsically high-dimensional data has recently been shown to exhibit substantial hubness in terms of skewness 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 hubness-aware learning methods have recently been proposed. This paper extends the existing fuzzy neighbor occurrence models in order to enable cost-sensitive learning. We evaluate the extended implementations within the context of multi-class boosting, which is used to learn the appropriate neighbor votes during the re-weighting iterations. The proposed approach is evaluated on a series of high-dimensional datasets from various domains. The results demonstrate promising improvements of the proposed approach over the baselines.
Starting Page	676
Ending Page	683
File Size	500283
Page Count	8
File Format	PDF
e-ISBN	9781479942749
DOI	10.1109/ICDMW.2014.38
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling boosting high-dimensional data Boosting k-nearest neighbor hubness machine learning Standards Equations hubness-aware classification Training data Data models Mathematical model
Content Type	Text
Resource Type	Article

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