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2 results on '"152202/14E, Hong Kong General Research"'

1. Transfer Clustering Ensemble Selection

Author

Hau-San Wong, Zhiwen Yu, C. L. Philip Chen, Yifan Shi, Jane You, Yide Wang, Jun Zhang, South China University of Technology [Guangzhou] (SCUT), University of Macau (UMac), The Hong Kong Polytechnic University [Hong Kong] (POLYU), City University of Hong Kong [Hong Kong] (CUHK), Institut d'Électronique et des Technologies du numéRique (IETR), 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)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ), 61751202, National Natural Science Foundation of China, 2017A030313355, Natural Science Foundation of Guangdong Province, G-YM05, Hong Kong Polytechnic University, Guangdong Province Higher Vocational Colleges and Schools Pearl River Scholar Funded Scheme 2018, 201704030051, Guangzhou Science and Technology Planning Project, CityU 11300715, Research Grants Council of the Hong Kong, 152202/14E, Hong Kong General Research, Université de Nantes (UN)-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), and Nantes Université (NU)-Université de Rennes 1 (UR1)

Subjects
Imagination, [SPI.OTHER]Engineering Sciences [physics]/Other, Computer science, media_common.quotation_subject, 02 engineering and technology, computer.software_genre, Clustering ensemble selection (CES), Search engine, Redundancy (information theory), 020204 information systems, transfer learning, 0202 electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Cluster analysis, media_common, multiobjective, Ensemble selection, Computer Science Applications, [SPI.TRON]Engineering Sciences [physics]/Electronics, Human-Computer Interaction, machine learning, Control and Systems Engineering, Task analysis, 020201 artificial intelligence & image processing, Data mining, Transfer of learning, computer, Software, Information Systems
Abstract

International audience; Clustering ensemble (CE) takes multiple clusteringsolutions into consideration in order to effectively improve theaccuracy and robustness of the final result. To reduce redundancyas well as noise, a CE selection (CES) step is added to furtherenhance performance. Quality and diversity are two importantmetrics of CES. However, most of the CES strategies adoptheuristic selection methods or a threshold parameter setting toachieve tradeoff between quality and diversity. In this paper, wepropose a transfer CES (TCES) algorithm which makes use of therelationship between quality and diversity in a source dataset, andtransfers it into a target dataset based on three objective functions.Furthermore, a multiobjective self-evolutionary process isdesigned to optimize these three objective functions. Finally, weconstruct a transfer CE framework (TCE-TCES) based on TCESto obtain better clustering results. The experimental results on 12transfer clustering tasks obtained from the 20newsgroups datasetshow that TCE-TCES can find a better tradeoff between qualityand diversity, as well as obtaining more desirable clusteringresults.

Published
2020
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2. Progressive Semisupervised Learning of Multiple Classifiers

Author

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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