An adaptive fuzzy c-means clustering-based mixtures of experts model for unlabeled data classification
文献类型：期刊论文
| 作者 | Xing, Hong-Jie1,2,3; Hua, Bao-Gang1,2 |
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2008 |
| 卷号 | 71期号:4-6页码:1008-1021 |
| 关键词 | mixture of experts Gaussian neural network unlabeled data classification |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | EM ALGORITHM ; TIME-SERIES ; VALIDITY ; NETWORKS |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000253663800057 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/9699]  |
| 专题 | 自动化研究所_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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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