Influence of data discretization on efficiency of Bayesian classifier for authorship attribution

被引：16

作者：

Baron, Grzegorz ^{[1
]}

机构：

[1] Silesian Tech Univ, PL-44100 Gliwice, Poland

来源：

KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 18TH ANNUAL CONFERENCE, KES-2014 | 2014年 / 35卷

关键词：

Bayesian classifier; Naive Bayes; stylometry; authorship attribution; text analysis; classification; discretization; binarization; DECISION TREE; NAIVE;

D O I：

10.1016/j.procs.2014.08.201

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Authorship attribution is one of the research areas in data mining domain and various methods can be employed for performing that task. The paper presents results of research on influence of data discretization on efficiency of Naive Bayes classifier. The analysis has been carried on datasets founded on texts of two male and two female authors using the WEKA data mining software framework. The binary classification was performed separately for both datasets for wide range of parameters of discretization process in order to investigate dependency between ways of discretization and quality of classification using Naive Bayes method. The numerical results of tests have been compared and discussed and some observations and conclusions formulated. (C) 2014 The Authors. Published by Elsevier B. V.

引用

页码：1112 / 1121

页数：10

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