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- Title
CLOVER: a faster prior-free approach to rare-category detection.
- Authors
Huang, Hao; He, Qinming; Chiew, Kevin; Qian, Feng; Ma, Lianhang
- Abstract
Rare-category detection helps discover new rare classes in an unlabeled data set by selecting their candidate data examples for labeling. Most of the existing approaches for rare-category detection require prior information about the data set without which they are otherwise not applicable. The prior-free algorithms try to address this problem without prior information about the data set; though, the compensation is high time complexity, which is not lower than $$O(dN^2)$$ where $$N$$ is the number of data examples in a data set and $$d$$ is the data set dimension. In this paper, we propose CLOVER a prior-free algorithm by introducing a novel rare-category criterion known as local variation degree (LVD), which utilizes the characteristics of rare classes for identifying rare-class data examples from other types of data examples and passes those data examples with maximum LVD values to CLOVER for labeling. A remarkable improvement is that CLOVER's time complexity is $$O(dN^{2-1/d})$$ for $$d > 1$$ or $$O(N\log N)$$ for $$d = 1$$. Extensive experimental results on real data sets demonstrate the effectiveness and efficiency of our method in terms of new rare classes discovery and lower time complexity.
- Subjects
HISTOGRAMS; SLOAN Digital Sky Survey; REMOTE sensing; STATISTICAL models; NEAREST neighbor analysis (Statistics)
- Publication
Knowledge & Information Systems, 2013, Vol 35, Issue 3, p713
- ISSN
0219-1377
- Publication type
Article
- DOI
10.1007/s10115-012-0530-9