• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Liu Yanfang, Li Wenbin, Gao Yang. Passive-Aggressive Learning with Feature Evolvable Streams[J]. Journal of Computer Research and Development, 2021, 58(8): 1575-1585. DOI: 10.7544/issn1000-1239.2021.20210330
Citation: Liu Yanfang, Li Wenbin, Gao Yang. Passive-Aggressive Learning with Feature Evolvable Streams[J]. Journal of Computer Research and Development, 2021, 58(8): 1575-1585. DOI: 10.7544/issn1000-1239.2021.20210330

Passive-Aggressive Learning with Feature Evolvable Streams

Funds: This work was supported by the National Key Research and Development Program of China (2018AAA0100905), the Education Scientific Research Project of Young Teachers of Fujian Province (JAT190743), and the Science and Technology Project of Longyan City (2019LYF13002, 2019LYF12010).
More Information
  • Published Date: July 31, 2021
  • In many real-world applications, data are collected in the form of a feature evolvable stream. For instance, old features of data gathered by limited-lifespan sensors disappear and new features emerge at the same time along with the sensors exchanging simultaneously. Online passive-aggressive algorithms have proven to be effective in learning linear classifiers from datasets with both a fixed feature space and a trapezoidal feature space. Therefore, in this paper we propose a new feature evolvable learning based on passive-aggressive update strategy (PAFE), which utilizes the margin to modify the current classifier. The proposed algorithm learns two models through passive-aggressive update strategy from the current features and recovered features of the vanished features. Specifically, we both recover the vanished features and mine the initialization of the current model from the overlapping periods in which both old and new features are available. Furthermore, we use two ensemble methods to improve performance: combining the predictions from the two models, and dynamically selecting the best single prediction. Experiments on both synthetic and real data validate the effectiveness of our proposed algorithm.
  • Related Articles

    [1]Zhang Zhenyu, Jiang Yuan. Label Noise Robust Learning Algorithm in Environments Evolving Features[J]. Journal of Computer Research and Development, 2023, 60(8): 1740-1753. DOI: 10.7544/issn1000-1239.202330238
    [2]Yang Wang, Gao Mingzhe, Jiang Ting. A Malicious Code Static Detection Framework Based on Multi-Feature Ensemble Learning[J]. Journal of Computer Research and Development, 2021, 58(5): 1021-1034. DOI: 10.7544/issn1000-1239.2021.20200912
    [3]Qi Qing, Cao Jian, Liu Yancen. The Evolution of Software Ecosystem in GitHub[J]. Journal of Computer Research and Development, 2020, 57(3): 513-524. DOI: 10.7544/issn1000-1239.2020.20190615
    [4]Ai Ke, Ma Guoshuai, Yang Kaikai, Qian Yuhua. A Classification Method of Scientific Collaborator Potential Prediction Based on Ensemble Learning[J]. Journal of Computer Research and Development, 2019, 56(7): 1383-1395. DOI: 10.7544/issn1000-1239.2019.20180641
    [5]Guo Yingjie, Liu Xiaoyan, Wu Chenxi, Guo Maozu, Li Ao. U-Statistics and Ensemble Learning Based Method for Gene-Gene Interaction Detection[J]. Journal of Computer Research and Development, 2018, 55(8): 1683-1693. DOI: 10.7544/issn1000-1239.2018.20180365
    [6]Zhang Hu, Tan Hongye, Qian Yuhua, Li Ru, Chen Qian. Chinese Text Deception Detection Based on Ensemble Learning[J]. Journal of Computer Research and Development, 2015, 52(5): 1005-1013. DOI: 10.7544/issn1000-1239.2015.20131552
    [7]Gong Shu, Qu Youli, and Tian Shengfeng. Supervised Learning of an Automatic Noisy Semantic Unit Filter for Multi-Document Summarization[J]. Journal of Computer Research and Development, 2013, 50(4): 873-882.
    [8]Fu Zhongliang. A Universal Ensemble Learning Algorithm[J]. Journal of Computer Research and Development, 2013, 50(4): 861-872.
    [9]Li Ming and Zhou Zhihua. Online Semi-Supervised Learning with Multi-Kernel Ensemble[J]. Journal of Computer Research and Development, 2008, 45(12): 2060-2068.
    [10]Zhan Dechuan and Zhou Zhihua. Ensemble-Based Manifold Learning for Visualization[J]. Journal of Computer Research and Development, 2005, 42(9): 1533-1537.
  • Cited by

    Periodical cited type(4)

    1. 陈燕菲,刘三民. 面向特征演化数据流的增量学习方法研究. 重庆工商大学学报(自然科学版). 2025(01): 94-104 .
    2. 张震宇,姜远. 面向特征演变环境的标记噪声鲁棒学习算法. 计算机研究与发展. 2023(08): 1740-1753 . 本站查看
    3. 刘艳芳,李文斌,高阳. 特征演化的置信-加权学习方法. 软件学报. 2022(04): 1315-1325 .
    4. 刘兆清,古仕林,侯臣平. 面向特征继承性增减的在线分类算法. 计算机研究与发展. 2022(08): 1668-1682 . 本站查看

    Other cited types(2)

Catalog

    Article views (562) PDF downloads (351) Cited by(6)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return