A New Performance Evaluation Method for Two-Class Imbalanced Problems
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Detecting Abnormal Events via Hierarchical Dirichlet Processes
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Cost-Sensitive Learning Vector Quantization for Financial Distress Prediction
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Performance evaluation of classification methods in cultural modeling
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
Handling class imbalance problem in cultural modeling
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A stable credit rating model based on learning vector quantization
Intelligent Data Analysis
Classification of high dimensional and imbalanced hyperspectral imagery data
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
An approach to cognitive assessment in smart home
Proceedings of the 2011 workshop on Data mining for medicine and healthcare
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Effects of data set features on the performances of classification algorithms
Expert Systems with Applications: An International Journal
Researcher homepage classification using unlabeled data
Proceedings of the 22nd international conference on World Wide Web
Proceedings of the 19th international conference on Intelligent User Interfaces
Influence of class distribution on cost-sensitive learning: A case study of bankruptcy analysis
Intelligent Data Analysis
| Hi-index | 0.00 |
In real-world applications the number of examples in one class may overwhelm the other class, but the primary interest is usually on the minor class. Cost-sensitive learning has been deeded as a good solution to these class-imbalanced tasks, yet it is not clear how does the class-imbalance affect cost-sensitive classifiers. This paper presents an empirical study using 38 data sets, which discloses that class-imbalance often affects the performance of cost-sensitive classifiers: When the misclassification costs are not seriously unequal, cost-sensitive classifiers generally favor natural class distribution although it might be imbalanced; while when misclassification costs are seriously unequal, a balanced class distribution is more favorable.