Automatical Optimal Threshold Searching Algorithm Based on Bhattacharyya Distance and Support Vector Machine

Ren Junxiang, L. Lifang, He Jianfeng, Li Long
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Abstract

Medical activities produce a huge amounts of medical data which include a large number of redundant data. Feature selection is an effective method to remove the redundant data. Selecting the feature attributes needs to determine a threshold. Currently, the thresholding value is mainly set by one's determination manually, which may lead to inaccurate threshold. In this paper proposes forward an adaptive threshold method combining Bhattacharyya distance and support vector machines (SVM). The experimental results show that the proposed method not only makes a high credibility threshold, but also improves classification work efficiency.
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基于Bhattacharyya距离和支持向量机的自动最优阈值搜索算法
医疗活动产生大量的医疗数据,其中包含大量的冗余数据。特征选择是去除冗余数据的有效方法。选择特性属性需要确定一个阈值。目前的阈值设置主要是通过人工确定,可能导致阈值不准确。提出了一种结合Bhattacharyya距离和支持向量机(SVM)的自适应阈值方法。实验结果表明,该方法不仅具有较高的可信度阈值,而且提高了分类工作效率。
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