This paper proposes an efficient algorithm named CATION (rare category detection algorithm based on weighted boundary degree) for rare category detection. By employing a rare-category criterion known as weighted boundary degree (WBD), this algorithm can make use of reverse k-nearest neighbors to help find the boundary points of rare categories and selects the boundary points with maximum WBDs for labeling. Extensive experimental results demonstrate that this algorithm avoids the limitations of existing approaches, has a significantly better efficiency on discovering new categories in data sets, and effectively reduces runtime, compared against the existing approaches.