摘要
交互式数据探索是一组多样的发现式应用程序的关键技术,着重于交互、探索和发现;在许多场景和领域中广泛应用.以海量的学术文献数据探索为背景,对交互式数据探索的特征自适应技术进行研究.首先,提出一种适用于面向学术文献数据探索的特征自适应交互式数据探索框架FA-IDE(feature-adaptive interactive data exploration),在每次迭代过程中动态地调整特征子集,以满足用户兴趣多样性的需求.其次,针对该框架,提出特征子集的均匀度BFS(balance of feature subsets)评价准则,并给出了基于BFS的序列前向特征选择算法.再次,针对相关样本发现问题,提出划分等级建立方法,根据决策树模型对用户兴趣区域划分后,提出基于相似度的结果集排序策略.实验结果表明,所提出方法可有效提高用户探索效率和最终结果的准确性.
Interactive data exploration(IDE)is a key technique in a diverse set of discovery-based applications,which focuses on interaction,exploration and discovery and has a wide range of applications in many scenes and areas.The feature adaptive technology of interactive data exploration was studied in this paper with the background of massive academic literature data exploration.Firstly,a framework of interactive data exploration was presented,namely FA-IDE(feature-adaptive interactive data exploration)framework,which can dynamically adjust the subset of features during each iteration to meet the needs of the user′s interest diversity.Secondly,according to this framework,the evaluation criteria of the balance of feature subsets(BFS)were proposed in the stage of exploration and a sequence forward feature selection algorithm based on BFS was also given.Besides,for the phases of related sample discovery,a division level establishment method was proposed.According to the decision tree model which can divide the user interest area,a strategy of result set sorting based on similarity was proposed.The results of experiments show that the accuracy and efficiency of the proposed method have been effectively improved.
作者
王蒙湘
李芳芳
于戈
WANG Meng-xiang;LI Fang-fang;YU Ge(School of Computer Science&Engineering,Northeastern University,Shenyang 110169,China)
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2018年第12期1685-1690,共6页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(61472071)
中央高校基本科研业务费专项资金资助项目(N161604005)
辽宁省自然科学基金资助项目(2015020018)
关键词
交互式数据探索
主题提取
特征选择
样本发现
机器学习
interactive data exploration
topic extraction
feature selection
sample discovery
machine learning
