Explicitly representing expected cost: an alternative to ROC representation
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Robust Classification for Imprecise Environments
Machine Learning
Concept learning and the problem of small disjuncts
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Predicting Software Escalations with Maximum ROI
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Multi-Relational Classification in Imbalanced Domains
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
A Hybrid Approach Handling Imbalanced Datasets
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Expert Systems with Applications: An International Journal
CODE: a data complexity framework for imbalanced datasets
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
Customer Validation of Commercial Predictive Models
Proceedings of the 2010 conference on Data Mining for Business Applications
Good seed makes a good crop: accelerating active learning using language modeling
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
ClassySeg: a machine learning approach to automatic stroke segmentation
Proceedings of the Eighth Eurographics Symposium on Sketch-Based Interfaces and Modeling
Cluster-Based sampling approaches to imbalanced data distributions
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Technical Section: A machine learning approach to automatic stroke segmentation
Computers and Graphics
| Hi-index | 0.00 |
In a concept learning problem, imbalances in the distribution of the data can occur either between the two classes or within a single class. Yet, although both types of imbalances are known to affect negatively the performance of standard classifiers, methods for dealing with the class imbalance problem usually focus on rectifying the between-class imbalance problem, neglecting to address the imbalance occuring within each class. The purpose of this paper is to extend the simplest proposed approach for dealing with the between-class imbalance problem--random re-sampling--in order to deal simultaneously with the two problems. Although re-sampling is not necessarily the best way to deal with problems of imbalance, the results reported in this paper suggest that addressing both problems simultaneously is beneficial and should be done by more sophisticated techniques as well.