Paper Info
Title
Unobtrusive Sensing Incremental Social Contexts Using Fuzzy Class Incremental Learning.
Abstract
By utilizing captured characteristics of surrounding contexts through widely used Bluetooth sensor, user-centric social contexts can be effectively sensed and discovered by dynamic Bluetooth information. At present, state-of-the-art approaches for building classifiers can basically recognize limited classes trained in the learning phase; however, due to the complex diversity of social contextual behavior, the built classifier seldom deals with newly appeared contexts, which results in degrading the recognition performance greatly. To address this problem, we propose, an OSELM (online sequential extreme learning machine) based class incremental learning method for continuous and unobtrusive sensing new classes of social contexts from dynamic Bluetooth data alone. We integrate fuzzy clustering technique and OSELM to discover and recognize social contextual behaviors by real-world Bluetooth sensor data. Experimental results show that our method can automatically cope with incremental classes of social contexts that appear unpredictably in the real-world. Further, our proposed method have the effective recognition capability for both original known classes and newly appeared unknown classes, respectively.
Year
DOI
Venue
2015
10.1109/ICDM.2015.156
ICDM
Keywords
DocType
ISSN
Context-aware, Bluetooth, Social Contextual behavior, Online Sequential Extreme Learning Machine (OSELM), Class incremental learning
Conference
1550-4786
Citations 
PageRank 
References 
0
0.34
14
Authors
8
Name
Order
Citations
PageRank
Zhenyu Chen147025.35
Yiqiang Chen21446109.32
Xingyu Gao310614.95
Shuangquan Wang427222.46
Lisha Hu51037.45
Chenggang Clarence Yan6916.10
Nicholas D. Lane74247248.15
Chunyan Miao82307195.72