Computer Forensic Using Lazy Local Bagging Predictors

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.