Selective sampling methods in one-class classification problems

Authors:
Piotr Juszczak;Robert P. W. Duin
Affiliations:
Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands;Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

Citing 8
Cited 1

An introduction to computational learning theory

An introduction to computational learning theory
Selective Sampling Using the Query by Committee Algorithm

Machine Learning
Combining labeled and unlabeled data with co-training

COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Support vector domain description

Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Kernel Whitening for One-Class Classification

SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Selective Sampling with Redundant Views

Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Concept learning in the absence of counterexamples: an autoassociation-based approach to classification

Concept learning in the absence of counterexamples: an autoassociation-based approach to classification
Active learning with statistical models

Journal of Artificial Intelligence Research

Machine learning techniques and mammographic risk assessment

IWDM'10 Proceedings of the 10th international conference on Digital Mammography

Quantified Score

Hi-index	0.00

Visualization

Abstract

Selective sampling, a part of the active learning method, reduces the cost of labeling supplementary training data by asking only for the labels of the most informative, unlabeled examples. This additional information added to an initial, randomly chosen training set is expected to improve the generalization performance of a learning machine. We investigate some methods for a selection of the most informative examples in the context of one-class classification problems i.e. problems where only (or nearly only) the examples of the so-called target class are available. We applied selective sampling algorithms to a variety of domains, including real-world problems: mine detection and texture segmentation. The goal of this paper is to show why the best or most often used selective sampling methods for two- or multi-class problems are not necessarily the best ones for the one-class classification problem. By modifying the sampling methods, we present a way of selecting a small subset from the unlabeled data to be presented to an expert for labeling such that the performance of the retrained one-class classifier is significantly improved.