Feature selection for unlabeled data

Authors:
Chien-Hsing Chen
Affiliations:
Department of Information Management, Hwa Hsia Institute of Technology, Chung Ho, Taipei, Taiwan
Venue:
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Year:
2011

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Abstract

Feature selection has been explored extensively for several real-world applications. In this paper, we address a new solution of selecting a subset of original features for unlabeled data. The concept of our feature selection method is referred to a basic characteristic of clustering in that a data instance usually belongs in the same cluster with its geometrically nearest neighbors and belongs to different clusters with its geometrically farthest neighbors. In particular, our method uses instance-based learning for quantifying features in the context of the nearest and the farthest neighbors of every instance, such that using salient features can raise this characteristic. Experiments on several datasets demonstrated the effectiveness of our presented feature selection method.