Computer Forensic Using Lazy Local Bagging Predictors

被引：0

作者：

邱卫东 ^{[1
]}

鲍诚毅 ^{[1
]}

朱兴全 ^{[2
]}

机构：

[1] School of Information Security Engineering,Shanghai Jiaotong University,Shanghai 200240,China

[2] Department of Computer Science & Engineering,Florida Atlantic University,Boca Raton,FL 33431,USA

来源：

JournalofShanghaiJiaotongUniversity(Science) | 2009年 / 14卷 / 01期

基金：

中国国家自然科学基金;

关键词：

computer forensic; data mining; classification; lazy learning; bagging; ensemble learning;

D O I：

暂无

中图分类号：

TP399-C2 [];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper,we study the problem of employ ensemble learning for computer forensic.We propose a Lazy Local Learning based bagging(L~3B) approach,where base earners are trained from a small instance subset surrounding each test instance.More specifically,given a test instance x,L~3B first discovers x’s k nearest neighbours,and then applies progressive sampling to the selected neighbours to train a set of base classifiers,by using a given very weak(VW) learner.At the last stage,x is labeled as the most frequently voted class of all base classifiers.Finally,we apply the proposed L~3B to computer forensic.

引用

页码：94 / 97

页数：4

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