Abstract
Rare category detecion (RCD) aims to discover rare categories in a massive unlabeled data set with the help of a labeling oracle. A challenging task in RCD is to discover rare categories which are concealed by numerous data examples from major categories. Only a few algorithms have been proposed for this issue, most of which are on quadratic or cubic time complexity. In this paper, we propose a novel tree-based algorithm known as RCD-Forest with \(O(\varphi n \log {(n/s)})\) time complexity and high query efficiency where n is the size of the unlabeled data set. Experimental results on both synthetic and real data sets verify the effectiveness and efficiency of our method.
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Weng, H., Liu, Z., Chiew, K., He, Q. (2015). Rare Category Detection Forest. In: Zhang, S., Wirsing, M., Zhang, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2015. Lecture Notes in Computer Science(), vol 9403. Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_55
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DOI: https://doi.org/10.1007/978-3-319-25159-2_55
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