Boosting for Learning Multiple Classes with Imbalanced Class Distribution

Authors:
Yanmin Sun;Mohamed S. Kamel;Yang Wang
Affiliations:
University of Waterloo, Canada;University of Waterloo, Canada;Pattern Discovery Software Systems Ltd., Canada
Venue:
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Year:
2006

Citing 0
Cited 10

Risk prediction and risk factors identification from imbalanced data with RPMBGA+

Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
FAST: a roc-based feature selection metric for small samples and imbalanced data classification problems

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A direct boosting algorithm for the k-nearest neighbor classifier via local warping of the distance metric

Pattern Recognition Letters
A new weighted rough set framework based classification for Egyptian NeoNatal Jaundice

Applied Soft Computing
Using a boosted tree classifier for text segmentation in hand-annotated documents

Pattern Recognition Letters
GAB-EPA: a GA based ensemble pruning approach to tackle multiclass imbalanced problems

ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Coarse-to-fine multiclass learning and classification for time-critical domains

Pattern Recognition Letters
Predicting minority class for suspended particulate matters level by extreme learning machine

Neurocomputing
Enhanced and hierarchical structure algorithm for data imbalance problem in semantic extraction under massive video dataset

Multimedia Tools and Applications
Imbalanced evolving self-organizing learning

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Classification of data with imbalanced class distribution has posed a significant drawback of the performance attainable by most standard classifier learning algorithms, which assume a relatively balanced class distribution and equal misclassification costs. This learning difficulty attracts a lot of research interests. Most efforts concentrate on bi-class problems. However, bi-class is not the only scenario where the class imbalance problem prevails. Reported solutions for bi-class applications are not applicable to multi-class problems. In this paper, we develop a cost-sensitive boosting algorithm to improve the classification performance of imbalanced data involving multiple classes. One barrier of applying the cost-sensitive boosting algorithm to the imbalanced data is that the cost matrix is often unavailable for a problem domain. To solve this problem, we apply Genetic Algorithm to search the optimum cost setup of each class. Empirical tests show that the proposed cost-sensitive boosting algorithm improves the classification performances of imbalanced data sets significantly.