期刊文献+

二次检索

题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

年份

共找到2篇文章
< 1 >
每页显示 20 50 100
Legal Control over Big Data Criminal Investigation 被引量:2
1
作者 Cheng Lei 《Social Sciences in China》 2019年第3期189-204,共16页
The rapid growth of big data technology has become a major trend affecting the pattern of world development.Big data criminal investigation is a new type of criminal detection used extensively in the course of police ... The rapid growth of big data technology has become a major trend affecting the pattern of world development.Big data criminal investigation is a new type of criminal detection used extensively in the course of police practice at home and abroad.Its emergence indicates a trend in criminal justice towards ensuring security at the expense of privacy and exchanging rights for information.Big data criminal investigation highlights the backwardness and dysfunction of the traditional framework of legal norms,evident in doubts about the legal attributes of such investigation and the obvious limitations of techniques for distinguishing data content from metadata.This leaves a vacuum in the regulation of investigative power at the preliminary stage of investigation.Big data criminal investigation itself is a doubleedge sword;in order to forestall the possible abuses it may entail in terms of deep and broad interventions in basic civil rights,big data criminal investigation should be brought under the necessary legal control.We therefore propose adopting a dual regulatory approach comprising investigative and data norms,selectively adopting the traditional normative framework of the principle of legality and the principle of proportionality,and at the same time supplementing it with other legal principles and mechanisms concerning the protection of personal information and data. 展开更多
关键词 big data big data criminal investigation data mining personal information technical investigative measures
原文传递
Research on Personal Credit Risk Assessment Model Based on Instance-Based Transfer Learning
2
作者 Maoguang Wang Hang Yang 《International Journal of Intelligence Science》 2021年第1期44-55,共12页
Personal credit risk assessment is an important part of the development of financial enterprises. Big data credit investigation is an inevitable trend of personal credit risk assessment, but some data are missing and ... Personal credit risk assessment is an important part of the development of financial enterprises. Big data credit investigation is an inevitable trend of personal credit risk assessment, but some data are missing and the amount of data is small, so it is difficult to train. At the same time, for different financial platforms, we need to use different models to train according to the characteristics of the current samples, which is time-consuming. <span style="font-family:Verdana;">In view of</span><span style="font-family:Verdana;"> these two problems, this paper uses the idea of transfer learning to build a transferable personal credit risk model based on Instance-based Transfer Learning (Instance-based TL). The model balances the weight of the samples in the source domain, and migrates the existing large dataset samples to the target domain of small samples, and finds out the commonness between them. At the same time, we have done a lot of experiments on the selection of base learners, including traditional machine learning algorithms and ensemble learning algorithms, such as decision tree, logistic regression, </span><span style="font-family:Verdana;">xgboost</span> <span style="font-family:Verdana;">and</span><span style="font-family:Verdana;"> so on. The datasets are from P2P platform and bank, the results show that the AUC value of Instance-based TL is 24% higher than that of the traditional machine learning model, which fully proves that the model in this paper has good application value. The model’s evaluation uses AUC, prediction, recall, F1. These criteria prove that this model has good application value from many aspects. At present, we are trying to apply this model to more fields to improve the robustness and applicability of the model;on the other hand, we are trying to do more in-depth research on domain adaptation to enrich the model.</span> 展开更多
关键词 Personal Credit Risk big data Credit investigation Instance-Based Transfer Learning
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部