.png){kind=icon}
返回
Unsupervised binary feature construction method for networked data
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2018.12.030.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Networked data is data composed of network objects and links. Network objects are characterized by high dimensional attributes and by links indicating the relationships among these objects. However, traditional feature selection and feature extraction methods consider only attribute information, thus ignoring link information. In the presented work, we propose a new unsupervised binary feature construction method (NetBFC) for networked data that reconstructs attributes for each object by exploiting link information. By exploring similar objects in the network and associating them, our method increases the similarities between objects with high probability of being in the same group. The proposed method enables local attribute enrichment and local attribute selection for each object by aggregating the attributes of similar objects in order to deal with the sparsity of networked data. In addition, this method applies an attribute elimination phase to eliminate irrelevant and redundant attributes which decrease the performance of clustering algorithms. Experimental results on real-world data sets indicate that NetBFC significantly achieves better performance when compared to baseline methods. (C) 2018 Elsevier Ltd. All rights reserved.
Keyword:
Feature construction
Feature extraction
Feature selection
Link reconstruction
Social media
Networked data
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
Distribution-based aggregation for relational learning with identifier attributes.png)
MACHINE LEARNING
OAAI
MACHINE LEARNINGIF2.9
OAAI分享收藏Physiological Arousal of Beginning Counselors In Relation To Disabled And Non-Disabled Clients与残疾和非残疾客户有关的初级辅导员的生理唤醒
PREAI
分享收藏
查看更多内容