Abstract
Abstract |
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| We present a novel feature screening algorithm by deriving relevance measures from the decision boundary of Support Vector Machines. It alleviates the ``independence'' assumption of traditional screening methods, e.g. those based on Information Gain and Augmented Variance Ratio, without sacrificing computational efficiency. We applied the proposed method to a bottom-up approach for automatic cervical cancer detection in multispectral microscopic thin PAP smear images. An initial set of around 4,000 multispectral texture features is effectively reduced to a computationally manageable size. The experimental results show significant improvements in pixel-level classification accuracy compared to traditional screening methods. | |
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Publication |
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The paper at MICCAI'04:
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Last update: Nov 18, 2005 |