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Title: A model driven approach to imbalanced data sampling in medical decision making
Authors: Yin, H.-L.
Leong, T.-Y. 
Keywords: Imbalanced data learning
Model driven sampling
Random sampling
Synthetic Minority Over Sampling (SMOTE)
Issue Date: 2010
Citation: Yin, H.-L., Leong, T.-Y. (2010). A model driven approach to imbalanced data sampling in medical decision making. Studies in Health Technology and Informatics 160 (PART 1) : 856-860. ScholarBank@NUS Repository.
Abstract: Classification is an important medical decision support function that can be seriously affected by disproportionate class distribution in the training data. In medical decision making, the rate of misclassification and the cost of misclassifying a minority (positive) class as a majority (negative) class are especially high. In this paper, we propose a new model-driven sampling approach to balancing data samples. Most existing data sampling methods produce new data points based on local, deterministic information. Our approach extends the idea of generative sampling to produce new data points based on an induced probabilistic graphical model. We present the motivation and the design of the proposed algorithm, and compare it with two representative imbalanced data sampling approaches on four medical data sets varying in size, imbalance ratio, and dimension. The empirical study helped identify the challenges in imbalanced data problems in medicine, and highlighted the strengths and limitations of the relevant sampling approaches. Performance of the model driven approach is shown to be comparable with existing approaches; potential improvements could be achieved by incorporating domain knowledge. © 2010 IMIA and SAHIA. All rights reserved.
Source Title: Studies in Health Technology and Informatics
ISBN: 9781607505877
ISSN: 09269630
DOI: 10.3233/978-1-60750-588-4-856
Appears in Collections:Staff Publications

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