General MC: Estimating Boundary of Positive Class from Small Positive Data

Authors:
Hwanjo Yu
Affiliations:
-
Venue:
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Year:
2003

Citing 4
Cited 4

Partially Supervised Classification of Text Documents

ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
PEBL: positive example based learning for Web page classification using SVM

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
PEBL: Web Page Classification without Negative Examples

IEEE Transactions on Knowledge and Data Engineering
SVMC: single-class classification with support vector machines

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence

Learning to Classify Documents with Only a Small Positive Training Set

ECML '07 Proceedings of the 18th European conference on Machine Learning
Iterative extreme learning machine for single class classifier using general mapping convergence framework

ACS'06 Proceedings of the 6th WSEAS international conference on Applied computer science
Learning from positive and unlabeled examples with different data distributions

ECML'05 Proceedings of the 16th European conference on Machine Learning
Learning from data streams with only positive and unlabeled data

Journal of Intelligent Information Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Single-Class Classification (SCC) seeks to distinguishone class of data from the universal set of multiple classes.We propose a SCC method called General MC that estimatesan accurate classification boundary of positive classfrom small positive data using the distribution of unlabeleddata. Our theoretical and empirical analyses show that,as long as the distribution of unlabeled data is not highlyskewed in the feature space, General MC significantly outperformsother recent SCC methods when the positive dataset is highly under-sampled.