%0 Conference Proceedings
%T Pattern Discovery from Big Data of Food Sampling Inspections Based on Extreme Learning Machine
%+ Fudan University [Shanghai]
%+ Beijing Forestry University
%+ China Food and Drug Administration (CFDA)
%A Liu, Yi
%A Li, Xin
%A Wang, Jianxin
%A Chen, Feng
%A Wang, Junyu
%A Shi, Yiwei
%A Zheng, Lirong
%Z Part 4: Big Data Analytics
%< avec comité de lecture
%( Lecture Notes in Business Information Processing
%B 11th International Conference on Research and Practical Issues of Enterprise Information Systems (CONFENIS)
%C Shanghai, China
%Y A Min Tjoa
%Y Li-Rong Zheng
%Y Zhuo Zou
%Y Maria Raffai
%Y Li Da Xu
%Y Niina Maarit Novak
%I Springer International Publishing
%3 Research and Practical Issues of Enterprise Information Systems
%V LNBIP-310
%P 132-142
%8 2017-10-18
%D 2017
%R 10.1007/978-3-319-94845-4_12
%K Food sampling inspection
%K Big data
%K Extreme learning machine
%K Logistic regression
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X Food sampling programs are implemented from time to time in local areas or throughout the country in order to guarantee food safety and to improve food quality. The hidden patterns in the accumulated huge amount of data and their potential values are worthy to research. In this paper, Extreme learning machine (ELM) is employed on real data sets collected from the food safety inspections of China in recent two years, in order to mine the relationship between food quality and food category, manufacturing site and season, inspection site and season, and many other attributes. Experimental results indicate that the ELM approach has better prediction precision and generalization ability than Logistic regression that was adopted in preceding work. The patterns obtained are helpful for making more effective food sampling plans and for more targeted food safety tracing.
%G English
%Z TC 8
%Z WG 8.9
%2 https://inria.hal.science/hal-01888631/document
%2 https://inria.hal.science/hal-01888631/file/470174_1_En_12_Chapter.pdf
%L hal-01888631
%U https://inria.hal.science/hal-01888631
%~ SHS
%~ IFIP
%~ IFIP-TC
%~ IFIP-LNBIP
%~ IFIP-WG
%~ IFIP-TC8
%~ IFIP-WG8-9
%~ IFIP-CONFENIS
%~ IFIP-LNBIP-310