In practical application, the data are imbalanced, it is difficult to find the balanced, rather skewed data is the common occurrence. This poses a severe challenge to the classification algorithm. At present, imbalanced data classification methods are mainly for binary classes designed, and it is difficult to extend them to multiple classes. In this study, we introduced Lie group machine learning and proposed a semi-supervised learning algorithm based on the linear Lie group. First, the sample set is represented by a matrix, the isomorphism(or homomorphism)-GL(n) linear Lie group of the corresponding learning system is found, and the labeled data are used to represent the object to be learned by linear Lie group. Then, according to the algebraic structure of the linear Lie group, it is marked by the group method. We performed experiments on 18 benchmark multi-class imbalanced datasets to demonstrate the performance of our proposed method and measured the performance of multi-class imbalanced data using four state-of-the-art learning algorithms (mean of accuracy, mean of f-measure, and mean of area under the curve). The experimental results demonstrate that the proposed method is effective and improves the performance.

中文翻译：

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基于线性李群的不平衡多类分类半监督学习算法

在实际应用中，数据是不平衡的，很难找到平衡的，而偏斜的数据是常见的情况。这对分类算法提出了严峻的挑战。目前，不平衡数据分类方法主要是针对二进制类设计的，很难将其扩展到多个类。在这项研究中，我们介绍了李群机器学习，并提出了一种基于线性李群的半监督学习算法。首先，样本集由一个矩阵表示，同构（或同构）-GL（n）找到相应学习系统的线性李群，并将标注的数据用于表示线性李群要学习的对象。然后，根据线性李群的代数结构，用群法对其进行标记。我们在18个基准的多类不平衡数据集上进行了实验，以证明我们提出的方法的性能，并使用四种最新的学习算法（准确度，f均值的平均值）测量了多类不平衡数据的性能。 ，以及曲线下面积的平均值）。实验结果表明，该方法是有效的，并且可以提高性能。