Journal Article

Research on Digital Forensics Based on Uyghur Web Text Classification

Muming, Mutalipu, Abudurexiti, Ruxianguli, Jamal, Anwar, Aizezi, Yasen

2017

Overview

This paper mainly discusses the use of mutual information (MI) and Support Vector Machines (SVMs) for Uyghur Web text classification and digital forensics process of web text categorization: automatic classification and identification, conversion and pretreatment of plain text based on encoding features of various existing Uyghur Web documents etc., introduces the pre-paratory work for Uyghur Web text encoding. Focusing on the non-Uyghur characters and stop words in the web texts filtering, we put forward a Multi-feature Space Normalized Mutual Information (M-FNMI) algorithm and replace MI between single feature and category with mutual information (MI) between input feature combination and category so as to extract more accurate feature words; finally, we classify features with support vector machine (SVM) algorithm. The experimental result shows that this scheme has a high precision of classification and can provide criterion for digital forensics with specific purpose.

• Publisher: IGI Global
• Subject: Algorithms / Analysis / Classification / Coding / Computer forensics / Criminal statistics / Electronic documents / Encoding / Forensic computing / Forensic science / Forensic sciences / Information / Machinery / Support vector machines / Text categorization / Turkic languages / Uighurs / Uyghur people / Words (language)