RobustSPAM for inference from noisy longitudinal data and preservation of privacy

被引:1
作者
Palczewska, Anna [1 ]
Palczewski, Jan [2 ]
Aivaliotis, Georgios [2 ]
Kowalik, Lukasz [3 ]
机构
[1] Univ Leeds, Sch Geog, Leeds, W Yorkshire, England
[2] Univ Leeds, Sch Math, Leeds, W Yorkshire, England
[3] Univ Warsaw, Inst Informat, Warsaw, Poland
来源
2017 16TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA) | 2017年
基金
英国工程与自然科学研究理事会;
关键词
robust; temporal pattern; noisy data; privacy; ALGORITHM; KNOWLEDGE; PATTERNS;
D O I
10.1109/ICMLA.2017.0-137
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The availability of complex temporal datasets in social, health and consumer contexts has driven the development of pattern mining techniques that enable the use of classical machine learning tools for model building. In this work we introduce a robust temporal pattern mining framework for finding predictive patterns in complex timestamped multivariate and noisy data. We design an algorithm RobustSPAM that enables mining of temporal patterns from data with noisy timestamps. We apply our algorithm to social care data from a local government body and investigate how the efficiency and accuracy of the method depends on the level of noise. We further explore the trade-off between the loss of predictivity due to perturbation of timestamps and the risk of person re-identification.
引用
收藏
页码:344 / 351
页数:8
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