基于部分特权信息的支持向量机
Support Vector Machines Based on Incomplete Privileged Information
DOI: 10.12677/AAM.2020.93042, PDF,   
作者: 郑志敏：中国地质大学，数学与物理学院，湖北 武汉
关键词: 特权信息；融合；权值更新；Privileged Information； Fusion； Weight Updating
摘要: 特权信息的重要性在许多领域尤为突出，特别是在生物医学领域。许多研究表明，特权信息可以提高分类的准确性。传统方法是使用SVM+来解决包含特权信息的分类问题。但是在许多情况下，由于特权信息收集成本高昂，训练集仅包含部分特权信息。面对特权信息不完整的数据，最大的挑战是如何使用此部分特权信息来获取数据的隐藏信息。在文章中，我们提出的基于支持向量机(ICSVM+)部分特权信息的新融合分类算法，可以很好地解决这个问题。对此融合分类算法本文提出了两个思想，分别为交叉校正(CC)和线性加权(LW)去实现此融合算法。我们称这两种方法为CC-ICSVM+和LW-ICSVM+。另一项重要工作是，我们提出的基于窗口理论的权重更新方法能够很好地适应不同的数据集。通过与SVM和SVM+的实验比较，我们的ICSVM+方法能够有效地解决特权信息的缺失问题。
Abstract: The importance of privileged information is particularly prominent in many fields, especially in bio-medicine. It is because researchers have proven that privileged information can improve clas-sification accuracy. And the traditional approach usually uses SVM+ to solve the classification problem that contains privileged information. Unfortunately, in many cases the training data set contains only partial privileged information due to the high cost of privileged information collection. In the face of data with incomplete privilege information, the biggest challenge is how to use this partial privilege information to obtain the maximum hidden information of the data. Fortunately, a new fusion classification algorithm based on incomplete privileged information for Support Vector Machine (ICSVM+) that we proposed, can solve this problem well. In this paper, two ideas are proposed for the fusion classification algorithm, namely, cross-correction (CC) and linear weighting (LW). We call these two methods (CC) ICSVM+ and (LW) ICSVM+. Another important work is that a weight updating method based on window theory proposed by us can adapt well to different data sets. Through experimental comparison with SVM and SVM+, our ICSVM+ methods were able to effectively deal with the lack of privilege information.
文章引用：郑志敏. 基于部分特权信息的支持向量机[J]. 应用数学进展, 2020, 9(3): 349-358. https://doi.org/10.12677/AAM.2020.93042

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