Semi-supervised learning of class balance under class-prior change by distribution matching

Authors:
Marthinus Christoffel Du Plessis;Masashi Sugiyama
Affiliations:
-;-
Venue:
Neural Networks
Year:
2014

Citing 16
Cited 0

Support Vector Machines for Classification in Nonstandard Situations

Machine Learning
Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure

Neural Computation
Adjusting the Outputs of a Classifier to New a Priori Probabilities May Significantly Improve Classification Accuracy: Evidence from a multi-class problem in remote sensing

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Vehicle classification in distributed sensor networks

Journal of Parallel and Distributed Computing
Estimating class priors in domain adaptation for word sense disambiguation

ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Covariate Shift Adaptation by Importance Weighted Cross Validation

The Journal of Machine Learning Research
The foundations of cost-sensitive learning

IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
A Least-squares Approach to Direct Importance Estimation

The Journal of Machine Learning Research
Estimating divergence functionals and the likelihood ratio by convex risk minimization

IEEE Transactions on Information Theory
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
Statistical analysis of kernel-based least-squares density-ratio estimation

Machine Learning
Density Ratio Estimation in Machine Learning

Density Ratio Estimation in Machine Learning
Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation

Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation
Computational complexity of kernel-based density-ratio estimation: a condition number analysis

Machine Learning
Density-difference estimation

Neural Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In real-world classification problems, the class balance in the training dataset does not necessarily reflect that of the test dataset, which can cause significant estimation bias. If the class ratio of the test dataset is known, instance re-weighting or resampling allows systematical bias correction. However, learning the class ratio of the test dataset is challenging when no labeled data is available from the test domain. In this paper, we propose to estimate the class ratio in the test dataset by matching probability distributions of training and test input data. We demonstrate the utility of the proposed approach through experiments.