作　　者： ;

机构地区： 中山大学

出　　处： 《情报学报》 2019年第11期1150-1159,共10页

摘　　要： 学术会议作为科学评价的重要研究对象,同时也是传播学术成果的重要载体,它是某个学科发展到一定阶段的必然产物。在某些学科领域,学术会议因其知识传播的时效性,得到了众多学者的青睐。本文收集替代计量学指标Altmetrics.com和PlumX关于人工智能领域国际会议2007-2014年的相关数据,借助描述性统计与梯度提升决策树。通过指标筛选、数据不平衡问题处理、模型优化等步骤,形成一个基于梯度提升决策树的会议评价模型。本文使用新兴的替代计量学指标作为评价指标,同时结合当前热门的机器学习模型,有效地弥补传统文献计量学指标的不足,提升会议评价模型的准确率,丰富会议评价体系的相关研究并可作为后续研究的参考。 In order to spread and share academic achievements in a certain field, academic conferences serve as an inevitable and important part in the development of that subject field. In certain cases, academic conferences are favored by researchers because of their timely dissemination of knowledge. This paper collects relevant data from altmetrics indicators-Altmetrics.com and PlumX-on the international conferences on artificial intelligence from 2007 to 2014, with the help of descriptive statistics and gradient lifting decision tree. Using indicators screening, data imbalance processing, and model optimization,a meeting evaluation model based on the gradient lifting decision tree was formed. This model combines popular machine learning models with latest altmetrics indexes. It can effectively cover the deficiencies of traditional informetrics indicators and improve the accuracy of the conference evaluation model, which can enrich related research on conference evaluation and become a reference for future research.

关 键 词： 替代计量学 机器学习 梯度提升决策树 会议评价

分 类 号： [TP3]

领　　域： []