Abstract
Building models and learning patterns from a collection of data are essential tasks for decision making and dissemination of knowledge. One of the common tools to extract knowledge is to build a classifier. However, when the training dataset is sparse, it is difficult to build an accurate classifier. This is especially true in biological science, as biological data are hard to produce and error-prone. Through empirical results, this paper shows challenges in building an accurate classifier with a sparse biological training dataset. Our findings indicate the inadequacies in well known classification techniques. Although certain clustering techniques, such as seeded k-Means, show some promise, there are still spaces for further improvement. In addition, we propose a novel idea that could be used to produce more balanced classifier when training data samples are very limited.
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Zhang, M., Jiang, W., Clifton, C., Prabhakar, S. (2007). Identifying Rare Classes with Sparse Training Data. In: Wagner, R., Revell, N., Pernul, G. (eds) Database and Expert Systems Applications. DEXA 2007. Lecture Notes in Computer Science, vol 4653. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74469-6_73
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DOI: https://doi.org/10.1007/978-3-540-74469-6_73
Publisher Name: Springer, Berlin, Heidelberg
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