A self learning rough fuzzy neural network classifier for mining temporal patterns
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Bidirectional semi-supervised learning with graphs
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
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We propose a framework for improving classifier performance by effectively using auxiliary samples. The auxiliary samples are labeled not in terms of the target taxonomy according to which we wish to classify samples, but according to classification schemes or taxonomies that are different from the target taxonomy. Our method finds a classifier by minimizing a weighted error over the target and auxiliary samples. The weights are defined so that the weighted error approximates the expected error when samples are classified into the target taxonomy. Experiments using synthetic and text data show that our method significantly improves the classifier performance in most cases compared to conventional data augmentation methods.