期刊：
2015 International Conference on Logistics, Informatics and Service Sciences (LISS),2015年:1-6
通讯作者：
Xiao, Yi
作者机构：
[Sun, Haiyan; Xiao, Jin] Sichuan Univ, Sch Business, Chengdu, Peoples R China.;[Hu, Yi] Univ Chinese Acad Sci, Sch Management, Beijing, Peoples R China.;[Xiao, Yi] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
通讯机构：
[Xiao, Yi] C;Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
会议名称：
International Conference on Logistics, Informatics and Service Sciences (LISS)
会议时间：
JUL 27-29, 2015
会议地点：
Beijing Jiaotong Univ, Int Ctr Informat Res, Barcelona, SPAIN
会议主办单位：
Beijing Jiaotong Univ, Int Ctr Informat Res
关键词：
energy demand;small sample prediction;auto-regressive;group method of data handling
摘要：
It is very significant for us to predict future energy consumption accurately. As for China's energy consumption annual time series, the sample size is relatively small. This study combines the traditional auto-regressive model with group method of data handling (GMDH) suitable for small sample prediction, and proposes a novel GMDH based auto-regressive (GAR) model. This model can finish the modeling process in self-organized manner, including finding the optimal complexity model, determining the optimal auto-regressive order and estimating model parameters. Further, four different GAR models, AS-GAR, MR-GAR, SRMSE-GAR and SMAPE-GAR, are constructed according to different external criteria. We conduct empirical analysis on three energy consumption time series, including the total energy consumption, the total petroleum consumption and the total gas consumption. The results show that AS-GAR model has the best forecasting performance among the four GAR models, and it outperforms ARIMA model, BP neural network model, SVM regression model and GM (1, 1) model. Finally, we give the out of sample prediction from 2014 to 2020 by GAR model.
期刊：
Journal of Systems and Information Technology,2015年17(4):351-363 ISSN：1328-7265
通讯作者：
Gan, C.
作者机构：
[Chunmei Gan] School of Information Management, Sun Yat-sen University, Guangzhou, China;[Weijun Wang] School of Information Management, Central China Normal University, Wuhan, China
通讯机构：
School of Information Management, Sun Yat-sen University, Guangzhou, China
关键词：
Gratification;Microblog;Motivation;Social media;Uses and gratifications;WeChat
作者机构：
[Gan, Chunmei] Sun Yat Sen Univ, Sch Informat Management, Guangzhou 510006, Guangdong, Peoples R China.;[Wang, Weijun] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;[Gan, Chunmei] Sun Yat Sen Univ, Sch Informat Management, 132 Waihuandong Rd, Guangzhou 510006, Guangdong, Peoples R China.
通讯机构：
[Gan, Chunmei] S;Sun Yat Sen Univ, Sch Informat Management, 132 Waihuandong Rd, Guangzhou 510006, Guangdong, Peoples R China.
关键词：
Social media;Co-word analysis;Bibliometric analysis;Research status;Research trend;China
摘要：
This study aims to map the intellectual structure of social media research in China from 2006 to 2013. Bibliometric and co-word analysis were employed to reveal the characteristics and status on social media research in China. Data was collected from China Academic Journals Full-text Database during the period of 2006---2013. In bibliometric analysis, descriptors of years, themes, subjects, institutions and authors were applied to obtain the research characteristics of social media. In co-word analysis, hierarchical cluster analysis, strategic diagram and social network analysis were adopted. Main results show that, a total of 3178 CSSCI papers on social media have risen yearly and exponentially. The most and distinctive themes were microblog, blog, virtual community and social networking site. The most common subject was News and media, followed by Library, information and digital library, Computer software and application. Wuhan University, Renmin University of China and Nanjing University ranked the top three on the most publications. And the distribution of number of authors with different publications obeys power-law distribution. Moreover, the number of keyword frequency obeys power-law distribution. The core keywords include social media, traditional media, Internet, dissemination and user. There are ten research directions on social media in China, some of which are highly correlated. Generally, the relatively dispersive distribution of research topics suggests the imbalanced development on social media research in China. Some hot topics are well-developed and tend to be mature, a few topics have a great potential for further development, and many other topics are marginal and immature. This study aims to map the intellectual structure of social media research in China from 2006 to 2013. Bibliometric and co-word analysis were employed to reveal the characteristics and status on social media research in China. Data was collected from China Academic Journals Full-text Database during the period of 2006---2013. In bibliometric analysis, descriptors of years, themes, subjects, institutions and authors were applied to obtain the research characteristics of social media. In co-word analysis, hierarchical cluster analysis, strategic diagram and social network analysis were adopted. Main results show that, a total of 3178 CSSCI papers on social media have risen yearly and exponentially. The most and distinctive themes were microblog, blog, virtual community and social networking site. The most common subject was News and media, followed by Library, information and digital library, Computer software and application. Wuhan University, Renmin University of China and Nanjing University ranked the top three on the most publications. And the distribution of number of authors with different publications obeys power-law distribution. Moreover, the number of keyword frequency obeys power-law distribution. The core keywords include social media, traditional media, Internet, dissemination and user. There are ten research directions on social media in China, some of which are highly correlated. Generally, the relatively dispersive distribution of research topics suggests the imbalanced development on social media research in China. Some hot topics are well-developed and tend to be mature, a few topics have a great potential for further development, and many other topics are marginal and immature.
摘要：
Customer churn prediction is one of the key steps to maximize the value of customers for an enterprise. It is difficult to get satisfactory prediction effect by traditional models constructed on the assumption that the training and test data are subject to the same distribution, because the customers usually come from different districts and may be subject to different distributions in reality. This study proposes a feature-selection-based dynamic transfer ensemble (FSDTE) model that aims to introduce transfer learning theory for utilizing the customer data in both the target and related source domains. The model mainly conducts a two-layer feature selection. In the first layer, an initial feature subset is selected by GMDH-type neural network only in the target domain. In the second layer, several appropriate patterns from the source domain to target training set are selected, and some features with higher mutual information between them and the class variable are combined with the initial subset to construct a new feature subset. The selection in the second layer is repeated several times to generate a series of new feature subsets, and then, we train a base classifier in each one. Finally, a best base classifier is selected dynamically for each test pattern. The experimental results in two customer churn prediction datasets show that FSDTE can achieve better performance compared with the traditional churn prediction strategies, as well as three existing transfer learning strategies.
作者机构：
[Zhang Dabin] College of Mathematics and Information, South China Agricultural University;[Ye Jia; Zhou Zhigang] School of Information Management, Central China Normal University;[Luan Yuqi] School Information Management, Central China Normal University
通讯机构：
[Dabin Zhang] C;College of Mathematics and Information, South China Agricultural University, Guangzhou510642, China
关键词：
fruit fly optimization algorithm;differential evolution;optimization;global optimization
摘要：
In order to overcome the problem of low convergence precision and easily relapsing into local extremum in fruit fly optimization algorithm (FOA), this paper adds the idea of differential evolution to fruit fly optimization algorithm so as to optimizing and a algorithm of fruit fly optimization based on differential evolution is proposed (FOADE). Adding the operating of mutation, crossover and selection of differential evolution to FOA after each iteration, which can jump out local extremum and continue to optimize. Compared to FOA, the experimental results show that FOADE has the advantages of better global searching ability, faster convergence and more precise convergence.
作者：
Xiong, Hui Xiang（熊回香）;Wang, Chen Ling;Guo, Si Yuan
期刊：
Proceedings of the International Conference on Management, Information and Educational Engineering, MIEE 2014,2015年2:865-873
作者机构：
[Xiong, Hui Xiang; Wang, Chen Ling] School of Information Management, Central China Normal University, Wuhan, China;[Guo, Si Yuan] Economic and Management School, Wuhan University, Wuhan, China
