Back to Search
Start Over
LERI: Local Exploration for Rare-Category Identification.
- Source :
-
IEEE Transactions on Knowledge & Data Engineering . Sep2020, Vol. 32 Issue 9, p1761-1772. 12p. - Publication Year :
- 2020
-
Abstract
- To identify the data examples of rare categories that form small compact clusters in large data sets, existing approaches mostly require enough labeled data examples as a training set to learn a classifier, assuming that the rare-category clusters are spherical or nearly spherical. Nonetheless, a large enough training set is usually difficult to obtain in practice, and rare categories in many real-world applications often form small compact clusters with arbitrary shapes. In this paper, we investigate how to identify all data examples of a rare category with an arbitrary shape based on only one seed (i.e., a labeled rare-category data example). Instead of finding a compact and spherical local region around the seed, we locally explore the data set from the seed by continuously searching and visiting the $k$ k -nearest neighbors of each newly visited data example. The local exploration connects the data examples in the objective rare category by the relationship of $k$ k -nearest neighbors, and meanwhile, suspected external data examples are filtered out if they are not close enough to any visited data example. Experimental results on both synthetic and real-world data sets are conducted, and the results verify the effectiveness and efficiency of our approach. [ABSTRACT FROM AUTHOR]
- Subjects :
- *K-nearest neighbor classification
*BIG data
Subjects
Details
- Language :
- English
- ISSN :
- 10414347
- Volume :
- 32
- Issue :
- 9
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Knowledge & Data Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 145130672
- Full Text :
- https://doi.org/10.1109/TKDE.2019.2911941