Measures of Geometrical Complexity in Classification Problems
Measures of Geometrical Complexity in Classification Problems
复制标题
分类问题中几何复杂性的度量
DOI：
10.1007/978-1-84628-172-3_1
复制
发表时间：
2006
影响因子：
2.4
通讯作者：
Martin H. C. Law
中科院分区：
文献类型：
--
作者：
T. Ho;M. Basu;Martin H. C. Law
当流行的分类器在实际应用中无法达到完美的准确性时，可能的原因可能是算法中的缺陷，数据中的内在困难以及方法和问题之间的不匹配。我们建议解决这个谜的发展措施的几何和拓扑特征的点集在高维空间。这些措施提供了一个基础，分析分类器的行为超出估计的错误率。我们讨论了几种措施，这种特性，和他们的效用在分析数据集与已知的或控制的复杂性。我们的观察证实了它们的有效性，并提出了几个未来的方向。
When popular classifiers fail to perform to perfect accuracy in a practical application, possible causes can be deficiencies in the algorithms, intrinsic difficulties in the data, and a mismatch between methods and problems. We propose to address this mystery by developing measures of geometrical and topological characteristics of point sets in high-dimensional spaces. Such measures provide a basis for analyzing classifier behavior beyond estimates of error rates. We discuss several measures useful for this characterization, and their utility in analyzing data sets with known or controlled complexity. Our observations confirm their effectiveness and suggest several future directions.