User Assisted Substructure Extraction in Molecular Data Mining
MDA '08 Proceedings of the 3rd international conference on Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry
A Hybrid Approach Handling Imbalanced Datasets
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
An empirical comparison of repetitive undersampling techniques
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
A computer-aided detection system for clustered microcalcifications
Artificial Intelligence in Medicine
A multi-objective optimisation approach for class imbalance learning
Pattern Recognition
Effects of data set features on the performances of classification algorithms
Expert Systems with Applications: An International Journal
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Two class classification problems in real world are often characterized by imbalanced classes. This is a serious issue since a classifier trained on such a data distribution typically exhibits a prediction accuracy highly skewed towards the majority class. To improve the quality of the classifier, many approaches have been proposed till now for building artificially balanced training sets. Such methods are mainly based on undersampling the majority class and/or oversampling the minority class. However, both approaches can produce overfitting or underfitting problems for the trained classifier. In this paper we present a method for building a multiple classifier system in which each constituting classifier is trained on a subset of the majority class and on the whole minority class. The approach has been tested on the detection of microcalcifications on digital mammograms. The results obtained confirm the effectiveness of the method.