Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (21): 195-202.

### Fusion Weight Mechanism and Improved SDIM Partial Label Classification Algorithm

ZHANG Huiting, XIE Hongwei, ZHOU Hui, ZHANG Hao

1. 1.College of Software, Taiyuan University of Technology, Taiyuan 030024, China
2.College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
• Online:2021-11-01 Published:2021-11-04

### 融合权重机制和改进SDIM的偏标记分类算法

1. 1.太原理工大学 软件学院，太原 030024
2.太原理工大学 信息与计算机学院，太原 030024

Abstract:

The meaning of partial label learning is that the only true label is hidden in a group of candidate labels, whose purpose is to disambiguate the candidate labels and finally pick up the true label. The existing methods only take unilateral consideration of the similarity or difference between instances, so when the number of candidate labels have a sharpen increase, the accuracy of disambiguation and classification will be drop significantly. In response to the above problems, this paper proposes the fusion weight mechanism and improves SDIM partial label classification algorithm. On the basis of the original SDIM（Partial Label Learning by Semantic Difference Maximization） algorithm, it is added to minimize the Euclidean distance between instances of the same category, the operation is used to minimize the semantic difference between instances of the same category and it takes account the similarity of the instances into learning. At the same time, the weight of each instance is calculated by solving the correlation coefficient maximization problem, and the weight mechanism is introduced into the disambiguation learning of instances of the same category, so the differences are fully considered. The experimental results on the UCI synthetic data set show that compared with the traditional algorithm, the disambiguation accuracy of this algorithm is increased by 0.211%~12.613%, and the classification accuracy is increased by 0.287%~25.695%.