CLOVER: a faster prior-free approach to rare-category detection

Huang, Hao, He, Qinming, Chiew, Kevin, Qian, Feng and Ma, Lianhang (2013) CLOVER: a faster prior-free approach to rare-category detection. Knowledge and Information Systems, 35 (3). pp. 713-736. ISSN 0219-1377

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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(dN2) 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(dN2−1/d) for d>1 or O(NlogN) 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.

Item Type: Article
Uncontrolled Keywords: Rare-category detection, local variation degree, histogram density estimation
Subjects: G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Ay Okpokam
Date Deposited: 25 Jun 2013 10:08
Last Modified: 13 Oct 2019 00:32

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