An adaptive fuzzy c-means clustering-based mixtures of experts model for unlabeled data classification

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-Jie 1,2,3; Hua, Bao-Gang 1,2
发表期刊	NEUROCOMPUTING
	2008
卷号	71 期号:4-6 页码:1008-1021
文章类型	Article
摘要	Compared 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.
关键词	Mixture Of Experts Gaussian Neural Network Unlabeled Data Classification
WOS标题词	Science & Technology ; Technology
关键词[WOS]	EM ALGORITHM ; TIME-SERIES ; VALIDITY ; NETWORKS
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000253663800057
引用统计	被引频次：22[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/9699
专题	09年以前成果
作者单位	1.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
第一作者单位	模式识别国家重点实验室
推荐引用方式 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.