A comparative study on feature selection and adaptive strategies for email foldering using the ABC-DynF framework

被引：4

作者：

Carmona-Cejudo, Jose M. ^{[1
]}

Castillo, Gladys ^{[3
]}

Baena-Garcia, Manuel ^{[2
]}

Morales-Bueno, Rafael ^{[1
]}

机构：

[1] Univ Malaga, Dept Comp Sci, E-29071 Malaga, Spain

[2] Clin Rincon, Malaga, Spain

[3] Univ Aveiro, Dept Math, P-3810193 Aveiro, Portugal

来源：

KNOWLEDGE-BASED SYSTEMS | 2013年 / 46卷

关键词：

Email foldering; Adaptive systems; Text mining; Feature selection; FEATURE-EXTRACTION;

D O I：

10.1016/j.knosys.2013.03.006

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Email foldering is a challenging problem mainly due to its high dimensionality and dynamic nature. This work presents ABC-DynF, an adaptive learning framework with dynamic feature space that we use to compare several incremental and adaptive strategies to cope with these two difficulties. Several studies have been carried out using datasets from the ENRON email corpus and different configuration settings of the framework. The main aim is to study how feature ranking methods, concept drift monitoring, adaptive strategies and the implementation of a dynamic feature space can affect the performance of Bayesian email classification systems. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：81 / 94

页数：14

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