ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2022, Vol. 59 ›› Issue (9): 2015-2026.doi: 10.7544/issn1000-1239.20210467

• 人工智能 • 上一篇    下一篇

基于tri-training和极限学习机的跨领域信任预测

王岩,童向荣   

  1. (烟台大学计算机与控制工程学院 山东烟台 264005) (yixinwon@163.com)
  • 出版日期: 2022-09-01
  • 基金资助: 
    国家自然科学基金项目(62072392,61972360);山东省重大科技创新工程项目(2019522Y020131)

Cross-Domain Trust Prediction Based on tri-training and Extreme Learning Machine

Wang Yan Tong Xiangrong   

  1. (School of Computer and Control Engineering, Yantai University, Yantai, Shandong 264005)
  • Online: 2022-09-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (62072392, 61972360) and the Major Innovation Project of Science and Technology of Shandong Province (2019522Y020131).

摘要: 信任预测常用于推荐系统及交易平台,大多数学者研究基于某一个网络的信任预测,目前由于大多数网络缺乏标签,所以通过某一网络预测另一个网络的社会关系很有必要.利用BP神经网络结合非对称tri-training构建模型的方法存在2个问题:1)BP神经网络反向传播调整误差需要时间长;2)该模型只有2个分类器产生伪标签,这需要设置专家阈值.针对该模型存在的结构和速度问题,提出了改进的基于tri-training和极限学习机的跨领域信任预测模型,结合tri-training模型和非对称tri-training模型进行类似迁移学习的方法对网络进行预测,采用速度更快的极限学习机分类器,根据“少数服从多数”的投票机制,利用tri-training模型产生伪标签.实验测试特殊特征加入的影响,且在6个数据集上与其他已有算法进行对比,实验表明模型在召回率和稳定性方面优于其他算法.

关键词: tri-training, 极限学习机, 投票, 迁移学习, 伪标签

Abstract: Trust prediction is often used in recommendation systems and trading platforms. Most scholars study trust prediction based on one network. At present, most networks lack tags. Therefore, it is necessary to predict the social relationship of another network through one network. There are two problems in the method of using BP neural network combined with asymmetric tri-training to build a model. The first problem is that it takes a long time for BP neural network to backpropagate the adjustment error, and the second problem is that the model has only two classifiers to generate pseudo labels, which requires an expert threshold. To solve the structure and speed of the model, an improved cross-domain trust prediction model based on tri-training and extreme learning machine is proposed, which combines the tri-training model and asymmetric tri-training model to perform similar transfer learning methods to predict the network. The classifier of the model uses extreme learning machine with a faster speed, the tri-training model to generate pseudo-labels, and a “minority obeys majority” voting mechanism. Experiments test the effect of whether to add special features, and compare the algorithm with other existing algorithms on six data sets. Experiments show that the model is superior to other algorithms in terms of recall and stability.

Key words: tri-training, extreme learning machine, vote, transfer learning, pseudo label

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