RGAN-EL: A GAN and ensemble learning-based hybrid approach for imbalanced data classification     

《Information processing & management》2023,60(2):103235

Imbalanced sample distribution is usually the main reason for the performance degradation of machine learning algorithms. Based on this, this study proposes a hybrid framework (RGAN-EL) combining generative adversarial networks and ensemble learning method to improve the classification performance of imbalanced data. Firstly, we propose a training sample selection strategy based on roulette wheel selection method to make GAN pay more attention to the class overlapping area when fitting the sample distribution. Secondly, we design two kinds of generator training loss, and propose a noise sample filtering method to improve the quality of generated samples. Then, minority class samples are oversampled using the improved RGAN to obtain a balanced training sample set. Finally, combined with the ensemble learning strategy, the final training and prediction are carried out. We conducted experiments on 41 real imbalanced data sets using two evaluation indexes: F1-score and AUC. Specifically, we compare RGAN-EL with six typical ensemble learning; RGAN is compared with three typical GAN models. The experimental results show that RGAN-EL is significantly better than the other six ensemble learning methods, and RGAN is greatly improved compared with three classical GAN models.  相似文献   

The effect of toxicity on COVID-19 news network formation in political subcommunities on Reddit: An affiliation network approach     

《International Journal of Information Management》2021

Political polarization remains perhaps the “greatest barrier” to effective COVID-19 pandemic mitigation measures in the United States. Social media has been implicated in fueling this polarization. In this paper, we uncover the network of COVID-19 related news sources shared to 30 politically biased and 2 neutral subcommunities on Reddit. We find, using exponential random graph modeling, that news sources associated with highly toxic – “rude, disrespectful” – content are more likely to be shared across political subreddits. We also find homophily according to toxicity levels in the network of online news sources. Our findings suggest that news sources associated with high toxicity are rewarded with prominent positions in the resultant network. The toxicity in COVID-19 discussions may fuel political polarization by denigrating ideological opponents and politicizing responses to the COVID-19 pandemic, all to the detriment of mitigation measures. Public health practitioners should monitor toxicity in public online discussions to familiarize themselves with emerging political arguments that threaten adherence to public health crises management. We also recommend, based on our findings, that social media platforms algorithmically promote neutral and scientific news sources to reduce toxic discussion in subcommunities and encourage compliance with public health recommendations in the fight against COVID-19.  相似文献   

复合式训练影响青少年下肢爆发力的meta分析     

翟华楠  周彤 《武汉体育学院学报》2020,54(10):65-71

目的：运用元分析的方法检验复合式训练对青少年下肢爆发力影响的干预效果，检验并提出最优的运动干预方案。方法：运用STATA 15.1和Review Manager 5.3对纳入的10篇文献和301名被试者通过设置抗阻类型、抗阻强度、间歇时间、训练频率和训练周期等5个调节变量进行亚组分析，采用固定效应模型进行整体效应、异质性、发表偏倚性检验。结果：（1）复合式训练对跳跃类运动表现的影响效应量d0.36，对短跑类运动表现的影响效应量d-0.51，两者达到中等效应且具有统计学意义；（2）本研究纳入的10篇文献异质性较低，间歇时间、训练频率和训练周期的对跳跃类和短跑类运动表现影响的效应量有一定差别。结论：（1）复合式训练能够显著提升青少年跳跃类和短跑类运动表现；（2）抗阻训练选择等张收缩、强度70%1RM以下、小于3分钟的间歇时间、在4~8周内选择每周1-2次的复合式训练能够对青少年运动表现有较好的提升；（3）围绕青少年人群开展的复合式训练较为缺乏，后续研究应加强理论与实践研究。  相似文献   

基于二模复杂网络的隐性知识发现方法研究——以潜在药物靶点挖掘为例     

李东巧  陈芳  韩涛  杨艳萍  王学昭  王燕鹏  Cynthia Liu  Yingzhu Li 《图书情报工作》2020,64(21):120-129

[目的/意义] 通过构建二模复杂网络模型，揭示隐藏在海量文献中的隐性知识。[方法/过程] 通过NetworkX复杂网络工具包，依据任意两个节点的共现关系构建二模复杂网络模型；对网络模型中节点的共现关系进行加权，计算网络的拓扑信息并进行AP聚类，提取节点间的直接关系；采用AUC方法对AA、JC、加权改进的wAA和wJC等4种链路预测算法进行评价，遴选出最合适的预测算法，并对复杂网络的隐性关系进行预测分析。[结果/结论] 以潜在药物靶点挖掘为例进行的实证研究结果表明，wAA链路预测算法为最优的链路预测算法；二模复杂网络模型、指标和方法体系在美国化学文摘社数据库中的药物靶点挖掘中具有一定的有效性。下一步计划在其他数据库中或其他研究领域中进行尝试，以进一步验证该模型的通用性和有效性。  相似文献   

From objects to subjects: Repositioning teachers as policy actors doing policy work     

《Teaching and Teacher Education》2018

Within policy discourses, teachers are positioned as objects of reform who enact policies under rational systems of observation and accountability and are, therefore, situated as de-professionalized actors lacking expert knowledge. This research project asks relatively straight-forward questions: If practicing teachers were given a voice in political debates over urban education policy and reform, what would they say? What macro-level policy problems would they identify as being important? Findings indicate the primary policy problems include Systemic Inequity and Bad Policy. The primary policy solutions include, Schools as a Community Resource; Shared Decision-Making; Contextual Goals; and Time, Space and Resources.  相似文献   

Location extraction from tweets     

Thi Bich Ngoc Hoang  Josiane Mothe 《Information processing & management》2018,54(2):129-144

Five hundred million tweets are posted daily, making Twitter a major social media platform from which topical information on events can be extracted. These events are represented by three main dimensions: time, location and entity-related information. The focus of this paper is location, which is an essential dimension for geo-spatial applications, either when helping rescue operations during a disaster or when used for contextual recommendations. While the first type of application needs high recall, the second is more precision-oriented. This paper studies the recall/precision trade-off, combining different methods to extract locations. In the context of short posts, applying tools that have been developed for natural language is not sufficient given the nature of tweets which are generally too short to be linguistically correct. Also bearing in mind the high number of posts that need to be handled, we hypothesize that predicting whether a post contains a location or not could make the location extractors more focused and thus more effective. We introduce a model to predict whether a tweet contains a location or not and show that location prediction is a useful pre-processing step for location extraction. We define a number of new tweet features and we conduct an intensive evaluation. Our findings are that (1) combining existing location extraction tools is effective for precision-oriented or recall-oriented results, (2) enriching tweet representation is effective for predicting whether a tweet contains a location or not, (3) words appearing in a geography gazetteer and the occurrence of a preposition just before a proper noun are the two most important features for predicting the occurrence of a location in tweets, and (4) the accuracy of location extraction improves when it is possible to predict that there is a location in a tweet.  相似文献   

组织管理面临的现实挑战及对理论研究的启示   总被引：6，自引：0，他引：6  

尚玉钒  席酉民  王洪涛 《预测》2002,21(2):5-9

本文对新的时代特征下传统的组织形式所表现出来的缺陷进行了分析,并在探索归纳组织管理理论最新进展的基础上,认识到当西方崇尚科学和理性的管理模式在面对“有人参与”的复杂系统而出现合成谬误和控制困难时,组织管理研究呈现出了东西方管理“整合”及软化趋势。进而提出,在这场由计算机并行处理等技术引发的有关“协调的变革”中,对于协调问题的关注势必激发对组织中所普遍存在的和谐机理研究的关注。  相似文献   

基于组合神经网络的聚合物质量预测   总被引：2，自引：0，他引：2  

潘海天  夏陆岳  蔡亦军  李伯耿 《科技通报》2004,20(4):298-302

介绍了一种将组合神经网络用于聚合物质量预测的方法．由定量数据建立的单一神经网络模型往往缺乏泛化能力，而使用组合神经网络模型则可以显著改善模型的泛化能力．由于在建立组合神经网络模型过程中，合适的组合权重对模型是否具有良好预测性能是非常重要的，因此采用了岭回归方法来选择合适的组合权重．所提出的方法已成功应用于PVC颗粒特性的预测研究中。研究结果表明，与单一神经网络模型相比，组合神经网络模型具有更佳的模型预测精度和鲁棒性．  相似文献   

关于大学科技园的一个网络分析   总被引：8，自引：0，他引：8  

王大洲 《科学学研究》2004,22(2):164-169

大学科技园是一种以大学为依托,由社会网络、商业网络和专业网络交叠而成的创新网络。成功的大学科技园不仅意味着相关行动者之间的互动学习,同时也意味着使这种互动学习得以可能的共同知识基础和合作规范。在这里,不断衍生的企业构成了技术创新的主体和科技园网络进化的中心环节。通过他们的持续创业,大学科技园网络得以融入到区域创新网络之中,并向全球网络延伸。其中所体现的,是可扩展的自生自发秩序,是学术场域中的大学和经济场域中的企业之间的共生共荣。  相似文献   

无线传感器网络协议与研究   总被引：2，自引：0，他引：2  

李凯 《中国科技信息》2007,(4):98-99,101

传感器网络集成了网络技术、通信技术、传感器技术以及微机电系统技术，可以用于收集和处理信息。本文介绍了无线传感器网络的结构和协议以及目前的研究现状和未来研究方向。  相似文献