Automated multi-label text categorization with VG-RAM weightless neural networks

被引:34
作者
De Souza, Alberto F. [1 ]
Pedroni, Felipe [1 ]
Oliveira, Elias [2 ]
Ciarelli, Patrick M. [3 ]
Henrique, Wallace Favoreto [1 ]
Veronese, Lucas [1 ]
Badue, Claudine [1 ]
机构
[1] Univ Fed Espirito Santo, Dept Informat, BR-29075910 Vitoria, ES, Brazil
[2] Univ Fed Espirito Santo, Dept Ciencia Informacao, BR-29075910 Vitoria, ES, Brazil
[3] Univ Fed Espirito Santo, Dept Engn Eletr, BR-29075910 Vitoria, ES, Brazil
关键词
VG-RAM weightless neural networks; Multi-label text categorization; Web page categorization; Categorization of economic activities; KNN;
D O I
10.1016/j.neucom.2008.06.028
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
In automated multi-label text categorization, an automatic categorization system should output a label set, whose size is unknown a priori, for each document under analysis. Many machine learning techniques have been used for building such automatic text categorization systems. in this paper, we examine virtual generalizing random access memory weightless neural networks (VG-RAM WNN), an effective machine learning technique which offers simple implementation and fast training and test, as a tool for building automatic multi-label text categorization systems. We evaluated the performance of VG-RAM WNN on two real-world problems:, (i) categorization of free-text descriptions of economic activities and (ii) categorization of Web pages, and compared our results with that of the multi-label lazy learning approach (Multi-Label K-Nearest Neighbors, ML-KNN). Our experimental comparative analysis showed that, on average, VG-RAM WNN either outperforms ML-KNN or show similar categorization performance. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:2209 / 2217
页数:9
相关论文
共 27 条
[1]
Aleksander I, 1998, BRAIN-LIKE COMPUTING AND INTELLIGENT INFORMATION SYSTEMS, P513
[2]
Aleksander I, 1966, IEEE ELECT LETT, V2, P231
[3]
[Anonymous], 1998, RAM BASED NEURAL NET
[4]
Learning multi-label scene classification [J].
Boutell, MR ;
Luo, JB ;
Shen, XP ;
Brown, CM .
PATTERN RECOGNITION, 2004, 37 (09) :1757-1771
[5]
BUENO R, 2003, APPL BIONICS BIOMECH, V1, P21
[6]
Genetic algorithms for approximate similarity queries [J].
Bueno, Renato ;
Traina, Agma J. M. ;
Traina, Caetano, Jr. .
DATA & KNOWLEDGE ENGINEERING, 2007, 62 (03) :459-482
[7]
Carneiro RV, 2006, LECT NOTES COMPUT SC, V4232, P427
[8]
Clare A., 2001, Lecture Notes in Computer Science, P42
[9]
*CNAE, 2003, 11 CNAE IBGE
[10]
COMITE FD, 2001, LECT NOTES COMPUTER, V2734, P35