Combining Semi-supervised Clustering and Classification Under a Generalized Framework

被引:0
|
作者
Jiang, Zhen [1 ,2 ]
Zhao, Lingyun [1 ]
Lu, Yu [1 ]
机构
[1] Jiangsu Univ, Sch Comp Sci & Commun Engn, Zhenjiang, Peoples R China
[2] Jiangsu Prov Big Data Ubiquitous Percept & Intelli, Zhenjiang, Peoples R China
基金
中国国家自然科学基金;
关键词
Co-training; Classification; Semi-supervised clustering; Cluster-splitting;
D O I
10.1007/s00357-024-09489-9
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Most machine learning algorithms rely on having a sufficient amount of labeled data to train a reliable classifier. However, labeling data is often costly and time-consuming, while unlabeled data can be readily accessible. Therefore, learning from both labeled and unlabeled data has become a hot topic of interest. Inspired by the co-training algorithm, we present a learning framework called CSCC, which combines semi-supervised clustering and classification to learn from both labeled and unlabeled data. Unlike existing co-training style methods that construct diverse classifiers to learn from each other, CSCC leverages the diversity between semi-supervised clustering and classification models to achieve mutual enhancement. Existing classification algorithms can be easily adapted to CSCC, allowing them to generalize from a few labeled data. Especially, in order to bridge the gap between class information and clustering, we propose a semi-supervised hierarchical clustering algorithm that utilizes labeled data to guide the process of cluster-splitting. Within the CSCC framework, we introduce two loss functions to supervise the iterative updating of the semi-supervised clustering and classification models, respectively. Extensive experiments conducted on a variety of benchmark datasets validate the superiority of CSCC over other state-of-the-art methods.
引用
收藏
页码:181 / 204
页数:24
相关论文
共 50 条
  • [21] A semi-supervised multiscale generalized-VAE framework for one-class classification
    Sharma, Renuka
    Awate, Suyash P.
    NEUROCOMPUTING, 2025, 620
  • [22] A semi-supervised machine learning framework for microRNA classification
    Hassani, Mohsen Sheikh
    Green, James R.
    HUMAN GENOMICS, 2019, 13 (Suppl 1) : 43
  • [23] A Semi-supervised Active Learning Framework for Image Classification
    Li, Han-yi
    Yang, Ming
    Kang, Nan-nan
    Yue, Lu-lu
    MECHATRONICS ENGINEERING, COMPUTING AND INFORMATION TECHNOLOGY, 2014, 556-562 : 4765 - 4769
  • [24] A semi-supervised machine learning framework for microRNA classification
    Mohsen Sheikh Hassani
    James R. Green
    Human Genomics, 13
  • [25] Combining Semi-Supervised and Active Learning for Hyperspectral Image Classification
    Li, Mingzhi
    Wang, Rui
    Tang, Ke
    2013 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING (CIDM), 2013, : 89 - 94
  • [26] Active Semi-Supervised Classification based on Multiple Clustering Hierarchies
    Batista, Antonio J. L.
    Campello, Ricardo J. G. B.
    Sander, Jorg
    PROCEEDINGS OF 3RD IEEE/ACM INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS, (DSAA 2016), 2016, : 11 - 20
  • [27] Data Stream Classification by Adaptive Semi-supervised Fuzzy Clustering
    Castellano, Giovanna
    Fanelli, Anna Maria
    ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, PT II, 2017, 10614 : 770 - 771
  • [28] Hierarchical Text Clustering and Categorisation using A Semi-Supervised Framework
    Mahyoub, Mohamed
    Hind, Jade
    Woods, David
    Wong, Carl
    Hussain, Abir
    Aljumeily, Dhiya
    12TH INTERNATIONAL CONFERENCE ON THE DEVELOPMENTS IN ESYSTEMS ENGINEERING (DESE 2019), 2019, : 153 - 159
  • [29] News Article Classification with Clustering using Semi-Supervised Learning
    Krishnamoorthy, Arjun
    Patil, Akshay Kishor
    Vasudevan, N.
    Pathari, Vinod
    2018 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), 2018, : 86 - 91
  • [30] A genetic semi-supervised fuzzy clustering approach to text classification
    Liu, H
    Huang, ST
    ADVANCES IN WEB-AGE INFORMATION MANAGEMENT, PROCEEDINGS, 2003, 2762 : 173 - 180