A classification scheme for applications with ambiguous data
A classification scheme for applications with ambiguous data
复制标题
具有模糊数据的应用程序的分类方案
DOI：
10.1109/ijcnn.2000.859412
复制
发表时间：
2000
期刊：
影响因子：
--
通讯作者：
A. Back
中科院分区：
文献类型：
--
作者：
T. Trappenberg;A. Back
我们针对包含模糊数据的应用提出了一种模式分类方案，也就是说，在这些应用中模式在特征空间中占据重叠区域。这种情况在数据有噪声和/或某些特征未知时经常出现。我们证明，首先借助训练数据检测那些模糊区域，然后在对测试集进行类别预测之前将这些区域的数据重新分类为模糊数据是有利的。通过一个简单的例子演示了该方案，并在两个实际应用中进行了基准测试。
We propose a scheme for pattern classifications in applications which include ambiguous data, that is, where pattern occupy overlapping areas in the feature space. Such situations frequently occur with noisy data and/or where some features are unknown. We demonstrate that it is advantageous to first detect those ambiguous areas with the help of training data and then to re-classify those data in these areas as ambiguous before making class predictions on test sets. This scheme is demonstrated with a simple example and benchmarked on two real world applications.