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Published in:
2013 | OriginalPaper | Chapter
Confusion Matrix Based Reweighting
Authors : Vincent Damian Warmerdam, Zoltán Szlávik
Published in: Contemporary Challenges and Solutions in Applied Artificial Intelligence
Publisher: Springer International Publishing
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This paper introduces a method to rebalance the output of classification algorithms using the corresponding confusion matrices. This is done by modifying the classification output, i.e. reweighting predictions, when they can be interpreted as probabilities. The method is evaluated and analyzed via experiments involving a number of classifiers and both standard and real life datasets. Our results show that confusion matrix based reweighting can be used to achieve certain kinds of balance in classification, while maintaining the same level of accuracy.