基于粗糙集约简的公共事件微博舆情趋势影响因素提取
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摘要
【目的/意义】解决舆情趋势影响因素存在的冗余和不确定性的问题,从特征选择上进一步提高预测模型的预测效果和泛化能力。【方法/过程】采用基于粗糙集的特征约简方法,从众多影响因素中生成可以有效预测微博舆情趋势的最佳微博特征集合。【结果/结论】实验结果表明,采用本研究提取的粗糙集最小约简特征集合,可以在不降低建模精度和预测能力的前提下简化所建模型的复杂性,提高微博舆情趋势预测的准确度。
【Purpose/significance】To solve the problem of redundance and uncertainty of influence factors for public opinions trends,and to promote the accuracy and stability of prediction model from attribution selection.【Method/process】We adopted a rough set reduction technique to select optimal subsets of influencing features from the original feature space.【Result/conclusion】Results show that the prediction model built with Rough Set Reduced Feature Groups can reduce model complexity and improve prediction accuracy without sacrificing modelling precision and prediction ability.
【Purpose/significance】To solve the problem of redundance and uncertainty of influence factors for public opinions trends,and to promote the accuracy and stability of prediction model from attribution selection.【Method/process】We adopted a rough set reduction technique to select optimal subsets of influencing features from the original feature space.【Result/conclusion】Results show that the prediction model built with Rough Set Reduced Feature Groups can reduce model complexity and improve prediction accuracy without sacrificing modelling precision and prediction ability.
引文
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20 M.Zhao,C.Fu,L.Ji,K.Tang,M.Zhou.Feature selection and parameter optimization for support vector machines:Anew approach based on genetic algorithm with feature chromosomes[J].Expert Systems with Applications,2011,38(5):5197-5204.
21范波,潘泉,张洪才,程咏梅.在信息融合系统中引入多智能体技术[J].计算机工程与应用,2003,39(22):100-102.
22 J.Pei,G.Yu,X.Tian,M.R.Donnelley.A new method for early detection of mass concern about public health issues[J].Journal of Risk Research,2017,20(4):516-532.
23 S.G.Small,L.Medsker.Review of information extraction technologies and applications[J].Neural Computing and Applications,2013,25(3-4):533-548.
2 B.Wu,H.Shen.Analyzing and predicting news popularity on Twitter[J].International Journal of Information Management,2015,35(6):702-711.
3王高飞,李明.基于AHP_模糊综合分析的移动社交网络舆情预警模型研究[J].现代情报,2017,37(1):41-45.
4何音,夏志杰,翟玥,罗梦莹.突发事件情境下影响媒体官方微博传播的因素研究--基于多分logistic回归[J].情报科学,2017,(4):43-47.
5 M.Hall,E.Frank,G.Holmes,B.Pfahringer,P.Reutemann,I.Witten.The WEKA data mining software:an update[J].ACM SIGKDD Explorations Newsletter,2009,11(1):10-18.
6 X.Hu,N.Cercone.Learning in Relational Databases:A Rough Set Approach[J].Computational Intelligence,1995,11(2):323-338.
7 H.Peng,F.Long,C.Ding.Feature selection based on mutual information criteria of max-dependency,max-relevance,and min-redundancy[J].IEEE Transactions on Pattern Analysis&Machine Intelligence2005,27(8):1226-1238.
8 Z.Xu,L.Huang,B.Yang.Efficient Attribute Reduction Algorithm Based on Skowron Discernibility Matrix[C]//US:International Workshop on Intelligent Systems and Applications,2009:1-4.
9 M.A.Hall.Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning[C]//US:Seventeenth International Conference on Machine Learning.2000:359-366.
10 M.Robnik-?ikonja,I.Kononenko.Theoretical and Empirical Analysis of ReliefF and RReliefF[J].Machine Learning,2003,53(1-2):23-69.
11 I.Guyon,J.Weston,S.Barnhill,V.Vapnik.Gene Selection for Cancer Classification using Support Vector Machines[J].Machine Learning,2002,46(1-3):389-422.
12谢婧,刘功申,苏波,高魁.社交网络中的用户转发行为预测[J].上海交通大学学报,2013,47(4):584-588.
13 Q.Dang,F.Gao,Y.D.Zhou.Early detection method for emerging topics based on dynamic bayesian networks in micro-blogging networks[J].Expert Systems with Applications,2016,(57):285-295.
14 M.Wang,G.Yu,D.Yu.Mining typical features for highly cited papers[J].Scientometrics,2011,87(3):695-706.
15 Z.Pawlak,J.Grzymalabusse,R.Slowinski,W.Ziarko.ROUGH SETS[M].New York:Assoc Computing Machinery,1995.
16 R.Susmaga.Reducts and constructs in classic and dominance-based rough sets approach[J].Information Sciences,2014,(271):45-64.
17 Z.Pawlak.Rough Sets:Theoretical Aspects of Reasoning about Data[M].Boston:Kluwer Acadonic Publish,1991.
18 H.K.Zare,M.B.Fakhrzad.Solving flexible flow-shop problem with a hybrid genetic algorithm and data mining:A fuzzy approach[J].Expert Systems with Applications,2011,38(6):7609-7615.
19 L.Ke,Z.Feng,Z.Ren.An efficient ant colony optimization approach to attribute reduction in rough set theory[J].Pattern Recognition Letters,2008,29(9):1351-1357.
20 M.Zhao,C.Fu,L.Ji,K.Tang,M.Zhou.Feature selection and parameter optimization for support vector machines:Anew approach based on genetic algorithm with feature chromosomes[J].Expert Systems with Applications,2011,38(5):5197-5204.
21范波,潘泉,张洪才,程咏梅.在信息融合系统中引入多智能体技术[J].计算机工程与应用,2003,39(22):100-102.
22 J.Pei,G.Yu,X.Tian,M.R.Donnelley.A new method for early detection of mass concern about public health issues[J].Journal of Risk Research,2017,20(4):516-532.
23 S.G.Small,L.Medsker.Review of information extraction technologies and applications[J].Neural Computing and Applications,2013,25(3-4):533-548.