Please wait a minute...
New Technology of Library and Information Service  2016, Vol. 32 Issue (1): 32-39    DOI: 10.11925/infotech.1003-3513.2016.01.06
Orginal Article Current Issue | Archive | Adv Search |
Combine Deep Belief Networks and Fuzzy Set for Recognition of Fraud Transaction
Liyi Zhang,Chang Liu()
School of Information Management, Wuhan University, Wuhan 430072, China
Download: PDF(647 KB)   HTML ( 58
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] To solve the problem of fraud transaction in e-commerce platform. [Methods] This paper proposes a method that combine Deep Belief Networks and fuzzy set based on consumers’ purchase history and reviews. Through recognizing the users in fraud transactions—cheaters to recognize the fraud transactions. [Results] Tested by experiments using the data crawled from Taobao.com, the accuracy can be achieved 89%. Compared with the shallow machine learning model, the comprehensive performance improves significantly. [Limitations] In contrast with the huge normal users and the users in fraud transactions, the experimental data in the paper is relatively small. And the test data only from Taobao.com, lack of the data from the other e-commerce platform to be validated. [Conclusions] The users in fraud transactions can be identified by the method, and the fraud transaction in e-commerce can be reduced.

Key wordsFraud transaction      Cheater recognition      Product reviews      Deep learning      Fuzzy set     
Received: 26 June 2015      Published: 04 February 2016