EM algorithms for PCA and SPCA
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Building Text Classifiers Using Positive and Unlabeled Examples
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Large Scale Detection of Irregularities in Accounting Data
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Local decomposition for rare class analysis
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph-Based Rare Category Detection
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Category detection using hierarchical mean shift
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A self-training approach to cost sensitive uncertainty sampling
Machine Learning
Learning to classify texts using positive and unlabeled data
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Evaluation of novelty metrics for sentence-level novelty mining
Information Sciences: an International Journal
Learning classifiers from imbalanced data based on biased minimax probability machine
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Rare Category Characterization
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
RADAR: rare category detection via computation of boundary degree
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Finding Rare Classes: Active Learning with Generative and Discriminative Models
IEEE Transactions on Knowledge and Data Engineering
Semi-supervised object recognition based on Connected Image Transformations
Expert Systems with Applications: An International Journal
Semi-supervised learning combining co-training with active learning
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Rare category discovery aims at identifying unlabeled data examples of rare categories in a given data set. The existing approaches to rare category discovery often need a certain number of labeled data examples as the training set, which are usually difficult and expensive to acquire in practice. To save the cost however, if these methods only use a small training set, their accuracy may not be satisfactory for real applications. In this paper, for the first time, we propose the concept of rare category exploration, aiming to discover all data examples of a rare category from a seed (which is a labeled data example of this rare category) instead of from a training set. To this end, we present an approach known as the FRANK algorithm which transforms rare category exploration to local community detection from a seed in a kNN (k-nearest neighbors) graph with an automatically selected k value. Extensive experimental results on real data sets verify the effectiveness and efficiency of our FRANK algorithm.