CASIA OpenIR  > 09年以前成果
An adaptive fuzzy c-means clustering-based mixtures of experts model for unlabeled data classification
Xing, Hong-Jie1,2,3; Hua, Bao-Gang1,2
Source PublicationNEUROCOMPUTING
2008
Volume71Issue:4-6Pages:1008-1021
SubtypeArticle
AbstractCompared with labeled data, unlabeled data are more readily available. Currently, classification of unlabeled data is an open issue, especially for the case of unknown class number. In this paper, we propose an adaptive fuzzy c-means (FCM)-based mixtures of experts model to deal with the problem. In this model, each mixture of experts (ME) consists of two expert networks and a gating network. Two experts, namely. Gaussian neural network (GNN) and sigmoid neural network (SNN), are selected as two candidates. Two phases are employed to construct the proposed model. First, the whole input space is partitioned into several clusters using the FCM clustering algorithm. The number of clusters can be determined adaptively by a cluster validity function. Second, the proposed model is trained by a small fraction of samples which are closer to their corresponding cluster centers. A numerical study is made on several synthetic and real-world data sets. Compared with the other four models, the proposed model exhibits better generalization ability in dealing with problems of unsupervised classification. The experimental results also show that the extension version of the proposed model for semi-supervised classification is comparable to the (CVSVM)-V-3 approach. (c) 2007 Elsevier B.V. All rights reserved.
KeywordMixture Of Experts Gaussian Neural Network Unlabeled Data Classification
WOS HeadingsScience & Technology ; Technology
WOS KeywordEM ALGORITHM ; TIME-SERIES ; VALIDITY ; NETWORKS
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000253663800057
Citation statistics
Cited Times:17[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/9699
Collection09年以前成果
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
2.Chinese Acad Sci, Beijing Grad Sch, Beijing, Peoples R China
3.Hebei Univ, Coll Math & Comp Sci, Baoding, Peoples R China
Recommended Citation
GB/T 7714
Xing, Hong-Jie,Hua, Bao-Gang. An adaptive fuzzy c-means clustering-based mixtures of experts model for unlabeled data classification[J]. NEUROCOMPUTING,2008,71(4-6):1008-1021.
APA Xing, Hong-Jie,&Hua, Bao-Gang.(2008).An adaptive fuzzy c-means clustering-based mixtures of experts model for unlabeled data classification.NEUROCOMPUTING,71(4-6),1008-1021.
MLA Xing, Hong-Jie,et al."An adaptive fuzzy c-means clustering-based mixtures of experts model for unlabeled data classification".NEUROCOMPUTING 71.4-6(2008):1008-1021.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Xing, Hong-Jie]'s Articles
[Hua, Bao-Gang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Xing, Hong-Jie]'s Articles
[Hua, Bao-Gang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Xing, Hong-Jie]'s Articles
[Hua, Bao-Gang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.