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Adaptive local hyperplane algorithm for learning small medical data sets
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DOI：10.1111/j.1468-0394.2009.00494.x.png)
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Abstract
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It is not unique that only a few samples from medical studies are available for knowledge discovery. Hence, a suitable classifier for the small data set learning problem is very interesting in medical work. In this paper, we experiment with the adaptive local hyperplane algorithm on small medical data sets. The experimental results on two cancer data sets demonstrate that the proposed classifier outperforms, on average, all the other four benchmarking classifiers for learning small data sets.
Keywords:
small data set learning
classification
cancer
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