Learning CP-Nets Structure From Preference Data Streams

被引：1
|
作者：
Liu, Zhaowei [1 ,2 ]
Zhong, Zhaolin [2 ]
Zhang, Chenghui [1 ]
Yu, Yanwei [2 ]
Liu, Jinglei [2 ]
机构：
[1] Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Shandong, Peoples R China
[2] Yantai Univ, Sch Comp Sci & Control Engn, Yantai 264005, Peoples R China
来源：
IEEE ACCESS | 2018年 / 6卷
基金：
中国国家自然科学基金;
关键词：
Preference learning; dynamic CP-net; data streams; incremental approach; GENERATION; NETWORK;
D O I：
10.1109/ACCESS.2018.2873087
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
With the sharp increase of digital data emerging at present, the data in new applications are generated fast. Continuous cumulative data have gradually become massive and difficult to be handled due to limited workspace and limited amount of time. The conventional learning conditional preference networks' algorithm cannot successfully process the data streams. In this paper, we introduce the model of learning CP-nets from preference data streams and formalize the question. Then, an incremental approach is presented through which we can learn the CP-nets with gradually increasing data streams. The proposed method is verified on simulated data and real data, and it is also compared with other works.
引用
收藏
页码：56716 / 56726
页数：11
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