论文标题
多级分类，具有模糊 - 特征观察：理论和算法
Multi-class Classification with Fuzzy-feature Observations: Theory and Algorithms
论文作者
论文摘要
多级分类的理论分析证明，现有的多级分类方法可以在测试集上具有高分类精度的分类器训练，当实例在培训和测试集中具有相同分布的训练和测试集时，可以在培训集中收集足够的实例。但是，尚未解决一个多级分类的限制：在仅可用观察结果时，如何提高多类分类问题的分类准确性。因此，在本文中，我们提出了一个新颖的框架，以解决一个新的现实问题，称为多级分类，具有不精确的观察结果（MCIMO），在那里我们需要在其中培训具有模糊的观测的分类器。首先，我们基于模糊的Rademacher复杂性对MCIMO问题进行了理论分析。然后，建立了基于支持向量机和神经网络的两种实用算法来解决提出的新问题。合成和现实世界数据集的实验验证了我们的理论分析的合理性以及所提出算法的疗效。
The theoretical analysis of multi-class classification has proved that the existing multi-class classification methods can train a classifier with high classification accuracy on the test set, when the instances are precise in the training and test sets with same distribution and enough instances can be collected in the training set. However, one limitation with multi-class classification has not been solved: how to improve the classification accuracy of multi-class classification problems when only imprecise observations are available. Hence, in this paper, we propose a novel framework to address a new realistic problem called multi-class classification with imprecise observations (MCIMO), where we need to train a classifier with fuzzy-feature observations. Firstly, we give the theoretical analysis of the MCIMO problem based on fuzzy Rademacher complexity. Then, two practical algorithms based on support vector machine and neural networks are constructed to solve the proposed new problem. Experiments on both synthetic and real-world datasets verify the rationality of our theoretical analysis and the efficacy of the proposed algorithms.
