ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2019, Vol. 56 ›› Issue (9): 1843-1850.doi: 10.7544/issn1000-1239.2019.20180847

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Positive and Unlabeled Generative Adversarial Network on POI Positioning

Tian Jiwei, Wang Jinsong, Shi Kai   

  1. (School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384) (Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology (Tianjin University of Technology), Tianjin 300384) (National Engineering Laboratory for Computer Virus Prevention and Control Technology (Tianjin University of Technology), Tianjin 300457)
  • Online:2019-09-10
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61272450), the Key Program of the Natural Science Foundation of Tianjin (18JCZDJC30700), and the Science and Technology Project of Tianjin (17ZXHLSY00060).

Abstract: With the rapid popularization of smart mobile devices, people rely more and more on location-based social networking service (LBSNS). Due to the high cost of data acquisition, point of interest (POI) positioning based on small data collection has become a big challenge. Recent research focuses on received signal strength (RSS) and simultaneous localization methods. Although there has been some research on POI positioning, the existing approaches do not discuss the problem of insufficient positive training samples. Based on the truthful positive data and a large amount of unlabeled data, a novel approach, called positive and unlabeled generative adversarial network (puGAN), is proposed. Firstly, we use positive and unlabeled method along with the generative adversarial network to effectively mine the hidden features of data. Secondly, based on the hidden features, we calibrate the positive data and unlabeled data, then treat them as the input of the discriminator. Finally, with the minimax of generator and discriminator, a POI-discriminator model is obtained. We evaluate the new method by analyzing ROC curve and the relationship between training error and testing error. The results of experiments show that the method we proposed can effectively solve the problem of insufficient positive samples and outperforms the traditional models of POI positioning, including one-class classifier, SVM and neural network.

Key words: data mining, point of interest (POI), positioning, positive and unlabeled, generative adversarial network (GAN)

CLC Number: