Abstract
The exponential growth of data dimensions presents an obstacle in informatics as data miners try to construct ever greater training sets to overcome the theoretical limitations of statistical learning theory. Machine learning models require a minimum set of samples within each label to develop a representative hypothesis. To overcome these bounds, we developed an algorithm that can extract samples from an auxiliary domain to augment the training set. Our work exploits concepts from the “Transfer Learning” and “Imbalanced Learning” domains to expand the training set and permit standard models to be applied. We present theoretical verification of our method and demonstrate the effectiveness of our framework with experimental results on real-world data.
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Al-Stouhi, S., Pandya, A. (2012). Over-Sampling from an Auxiliary Domain. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34475-6_69
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DOI: https://doi.org/10.1007/978-3-642-34475-6_69
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