Research of multi-sided multi-granular neural network ensemble optimization method
文献类型：期刊论文
| 作者 | Li, Hui3,4; Wang, Xuesong3; Ding, Shifei1,2 |
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2016-07-12 |
| 卷号 | 197页码:78-85 |
| 关键词 | Neural network ensemble(NNE) Multi-sided attribute granularity Feature selection |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2016.02.013 |
| 英文摘要 | According to the thought "divide and conquer" that human perceives complicated things from multi-side and multi-view and balances the final decision, this paper puts forward the multi-sided multi-granular neural network ensemble optimization method based on feature selection, which divides attribute granularity of dataset from multi-side, and structures multi-granular individual neural networks using different attribute granularity and the corresponding subsets. In this way, we can gain multi-granular individual neural networks with greater diversity, and get better performance of neural network ensemble(NNE). Firstly, use feature selection method to calculate the importance of each attribute, according to the average weight to choose some attributes whose average weight is greater than a certain threshold, to form an attribute granularity and the corresponding sample subset, thus to construct an individual neural network. If samples are not properly identified, this attribute granularity is weak for the generalization ability of the sample. Secondly, again calculate the importance of the attributes of samples not properly identified, choose the attributes that can generalize the corresponding samples better, and add to the last attribute granularity to form a new attribute granularity, and at the same time random choose two-thirds of sample subset to construct an individual neural network. In turn, one can get a series of attribute granularities and the corresponding sample subsets and a series of multi-granular individual neural networks. These attribute granularities and the corresponding sample subsets constructed from multi-side and multi-view with greater diversity can construct multi-granular individual neural networks with greater diversity. This method not only reduces the dimension of the dataset, but also makes the attribute granularity to identify the corresponding sample as large as possible. Finally, by calculating the diversity of each of the two individual neural networks, optimal selects some individual neural networks with greater diversity to ensemble. The simulation experiments show that our proposed method here, multi-side multi-granular neural network ensemble optimization method, can gain better performance. (C) 2016 Elsevier B.V. All rights reserved. |
| 资助项目 | National Natural Science Foundation of China[61379101] ; National Key Basic Research Program of China[2013CB329502] ; Natural Science Foundation of Jiangsu Normal University[14XLA12] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000376694700007 |
| 出版者 | ELSEVIER SCIENCE BV |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/8393]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Li, Hui |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, Jiangsu, Peoples R China 3.China Univ Min & Technol, Sch Informat & Elect Engn, Xuzhou 221116, Jiangsu, Peoples R China 4.Jiangsu Normal Univ, Sch Comp Sci & Technol, Xuzhou 221116, Jiangsu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Hui,Wang, Xuesong,Ding, Shifei. Research of multi-sided multi-granular neural network ensemble optimization method[J]. NEUROCOMPUTING,2016,197:78-85. |
| APA | Li, Hui,Wang, Xuesong,&Ding, Shifei.(2016).Research of multi-sided multi-granular neural network ensemble optimization method.NEUROCOMPUTING,197,78-85. |
| MLA | Li, Hui,et al."Research of multi-sided multi-granular neural network ensemble optimization method".NEUROCOMPUTING 197(2016):78-85. |
入库方式： OAI收割
来源：计算技术研究所
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