Computer Forensic Using Lazy Local Bagging Predictors

• 来源期刊: 上海交通大学学报（英文版）
• 学科: 工学
• 年，卷（期）: 2009,（1）
• 页码范围: 94-97
• 页数: 4页
• 分类号: TP309
• 语种: 英文
• DOI: 10.1007/s12204-009-0094-3

摘要

In this paper, we study the problem of employ ensemble learning for computer forensic. We propose a Lazy Local Learning based bagging (L3B) approach, where base learners are trained from a small instance subset surrounding each test instance. More specifically, given a test instance x, L3B 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 L3B to computer forensic.