Journal of Frontiers of Computer Science and Technology ›› 2020, Vol. 14 ›› Issue (11): 1879-1887.DOI: 10.3778/j.issn.1673-9418.1912073

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Maximize AUC for Positive-Unlabeled Classification and Incremental Algorithm

MA Yumin, WANG Shitong   

  1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Online:2020-11-01 Published:2020-11-09



  1. 江南大学 人工智能与计算机学院, 江苏 无锡 214122


Positive-unlabeled classification is referred to as PU classification. Since there are only positive samples and unlabeled samples, the traditional classification methods are not effective in PU classification. For this reason, this paper proposes to apply AUC (area under receiver operating characteristic curve) in traditional classification methods as an objective function to PU classification because of the relationship between AUC under PU classification and traditional classification. For making the data linearly separable, this paper uses Gaussian kernel function to map the original sample to high-dimensional space. Optimizing the AUC objective function to obtain an analytical solution avoids the trouble of multiple iterations, and can derive an incremental formula to speed up the operation speed. Experimental results show that the proposed algorithm achieves performance similar to an ideal support vector machine (SVM) whose labels are known for all positive and negative examples in the training set, and achieves rapid increments. It is a powerful tool for dealing with real problems.

Key words: machine learning, positive-unlabeled (PU) classification, AUC, incremental algorithm



关键词: 机器学习, PU分类, AUC, 增量算法