论文标题
应用于非均匀采样数据的一级分类器的动态决策边界
Dynamic Decision Boundary for One-class Classifiers applied to non-uniformly Sampled Data
论文作者
论文摘要
模式识别的一个典型问题是不均匀采样的数据，它修改了机器学习算法的一般性能和能力以进行准确的预测。通常，当数据在数据空间的特定区域中，数据不够，将数据视为不均匀采样，这使我们遇到了错误分类问题。这个问题减少了单级分类器的目标，从而降低了其性能。在本文中，我们提出了一个基于具有动态决策边界（OCDMST）的最小生成树的单级分类器，以便在我们具有不均匀采样的数据的情况下进行良好的预测。为了证明我们的方法的有效性和鲁棒性，我们将与最新的一级分类器相比，在大多数人中达到了最新的分类器。
A typical issue in Pattern Recognition is the non-uniformly sampled data, which modifies the general performance and capability of machine learning algorithms to make accurate predictions. Generally, the data is considered non-uniformly sampled when in a specific area of data space, they are not enough, leading us to misclassification problems. This issue cut down the goal of the one-class classifiers decreasing their performance. In this paper, we propose a one-class classifier based on the minimum spanning tree with a dynamic decision boundary (OCdmst) to make good prediction also in the case we have non-uniformly sampled data. To prove the effectiveness and robustness of our approach we compare with the most recent one-class classifier reaching the state-of-the-art in most of them.
