School of Computing and Intelligent Systems, University of Ulster, Londonderry BT487JL, UK.
IEEE Trans Pattern Anal Mach Intell. 2011 Jun;33(6):1189-201. doi: 10.1109/TPAMI.2010.188.
Pattern selection methods have been traditionally developed with a dependency on a specific classifier. In contrast, this paper presents a method that selects critical patterns deemed to carry essential information applicable to train those types of classifiers which require spatial information of the training data set. Critical patterns include those edge patterns that define the boundary and those border patterns that separate classes. The proposed method selects patterns from a new perspective, primarily based on their location in input space. It determines class edge patterns with the assistance of the approximated tangent hyperplane of a class surface. It also identifies border patterns between classes using local probability. The proposed method is evaluated on benchmark problems using popular classifiers, including multilayer perceptrons, radial basis functions, support vector machines, and nearest neighbors. The proposed approach is also compared with four state-of-the-art approaches and it is shown to provide similar but more consistent accuracy from a reduced data set. Experimental results demonstrate that it selects patterns sufficient to represent class boundary and to preserve the decision surface.
模式选择方法传统上是依赖于特定分类器来开发的。相比之下，本文提出了一种方法，用于选择关键模式，这些关键模式被认为携带适用于训练那些需要训练数据集空间信息的分类器的基本信息。关键模式包括定义边界的边缘模式和分离类别的边界模式。所提出的方法从一个新的角度选择模式，主要基于它们在输入空间中的位置。它使用类表面的近似切平面来确定类边缘模式。它还使用局部概率来识别类之间的边界模式。该方法使用包括多层感知器、径向基函数、支持向量机和最近邻在内的流行分类器在基准问题上进行了评估。所提出的方法还与四种最先进的方法进行了比较，结果表明它从较小的数据集提供了类似但更一致的准确性。实验结果表明，它选择的模式足以表示类边界并保留决策面。
