1. Progressive Semisupervised Learning of Multiple Classifiers
- Author
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Hau-San Wong, Yide Wang, Zhiwen Yu, Jun Zhang, Jane You, Guoqiang Han, Ye Lu, Southern University of Science and Technology [Shenzhen] (SUSTech), Department of computing, City University of Hong Kong [Hong Kong] (CUHK), Institut d'Électronique et des Technologies du numéRique (IETR), Nantes Université (NU)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), 61472145, NSFC, 7004674, City University of Hong Kong, G-YM05, Hong Kong Polytechnic University, S2013050014677, Guangdong Natural Science Funds for Distinguished Young Scholars, 2016B090918042, Science and Technology Planning Project of Guangdong Province, China, CityU 11300715, Research Grants Council of the Hong Kong Special Administrative Region, China, 152202/14E, Hong Kong General Research, Southern University of Science and Technology (SUSTech), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and Université de Nantes (UN)-Université de Rennes 1 (UR1)
- Subjects
[SPI.OTHER]Engineering Sciences [physics]/Other ,Boosting (machine learning) ,Computer science ,Active learning (machine learning) ,Stability (learning theory) ,02 engineering and technology ,Semi-supervised learning ,Machine learning ,computer.software_genre ,semisupervised learning ,Robustness (computer science) ,020204 information systems ,Ensemble learning ,0202 electrical engineering, electronic engineering, information engineering ,random subspace ,Electrical and Electronic Engineering ,Training set ,business.industry ,Nonparametric statistics ,Pattern recognition ,Linear subspace ,Computer Science Applications ,Human-Computer Interaction ,ComputingMethodologies_PATTERNRECOGNITION ,machine learning ,Control and Systems Engineering ,020201 artificial intelligence & image processing ,Algorithm design ,Artificial intelligence ,business ,computer ,optimization ,[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing ,Software ,Subspace topology ,Information Systems - Abstract
International audience; Semisupervised learning methods are often adopted to handle datasets with very small number of labeled samples. However, conventional semisupervised ensemble learning approaches have two limitations: 1) most of them cannot obtain satisfactory results on high dimensional datasets with limited labels and 2) they usually do not consider how to use an optimization process to enlarge the training set. In this paper, we propose the progressive semisupervised ensemble learning approach (PSEMISEL) to address the above limitations and handle datasets with very small number of labeled samples. When compared with traditional semisupervised ensemble learning approaches, PSEMISEL is characterized by two properties: 1) it adopts the random subspace technique to investigate the structure of the dataset in the subspaces and 2) a progressive training set generation process and a self evolutionary sample selection process are proposed to enlarge the training set. We also use a set of nonparametric tests to compare different semisupervised ensemble learning methods over multiple datasets. The experimental results on 18 real-world datasets from the University of California, Irvine machine learning repository show that PSEMISEL works well on most of the real-world datasets, and outperforms other state-of-the-art approaches on 10 out of 18 datasets.
- Published
- 2017
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