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

Neurocomputing

Volumn 72, Issue 10-12, 2009, Pages 2209-2217

  De Souza, Alberto F ^a   Pedroni, Felipe ^a   Oliveira, Elias ^a   Ciarelli, Patrick M ^a   Henrique, Wallace Favoreto ^a   Veronese, Lucas ^a   Badue, Claudine ^a  

^a FEDERAL UNIVERSITY OF ESPÍRITO SANTO   (Brazil)

Author keywords

Categorization of economic activities; Multi label text categorization; VG RAM weightless neural networks; Web page categorization

Indexed keywords

A-PRIORI; AUTOMATIC CATEGORIZATIONS; AUTOMATIC TEXT CATEGORIZATIONS; CATEGORIZATION OF ECONOMIC ACTIVITIES; COMPARATIVE ANALYSIS; K-NEAREST NEIGHBORS; LAZY LEARNING; MACHINE-LEARNING TECHNIQUES; MULTI-LABEL TEXT CATEGORIZATION; RANDOM ACCESS MEMORIES; REAL-WORLD PROBLEMS; TEXT CATEGORIZATION SYSTEMS; VG-RAM WEIGHTLESS NEURAL NETWORKS; WEB PAGE CATEGORIZATION;

EDUCATION; LABELS; LEARNING ALGORITHMS; RANDOM ACCESS STORAGE; ROBOT LEARNING; TEXT PROCESSING; WEBSITES;

NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; COMPUTER PROGRAM; CONTROLLED STUDY; ECONOMIC ASPECT; INTERMETHOD COMPARISON; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; PRIORITY JOURNAL;

EID: 67349255290     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2008.06.028     Document Type: Article

References (27)

1. Sebastiani F. Machine learning in automated text categorization. ACM Computing Surveys 34 1 (2002) 1-47
2. A. McCallum, Multi-label text classification with a mixture model trained by EM, in: Working Notes of the AAAI'99 Workshop on Text Learning, 1999, pp. 1-7.
3. Schapire R.E., and Singer Y. BoosTexter: a boosting-based system for text categorization. Machine Learning 39 2/3 (2000) 135-168
4. Clare A., and King R.D. Knowledge discovery in multi-label phenotype data. Lecture Notes in Computer Science vol. 2168 (2001), Springer, Berlin 42-53
5. Elisseeff A., and Weston J. A kernel method for multi-labelled classification. Advances in Neural Information Processing Systems vol. 14 (2002), MIT Press, Cambridge, MA 681-687
6. Comité F.D., Gilleron R., and Tommasi M. Learning multi-label alternating decision tree from texts and data. Lecture Notes in Computer Science vol. 2734 (2001), Springer, Berlin 35-49
7. Ueda N., and Saito K. Parametric mixture models for multi-label text. Advances in Neural Information Processing Systems vol. 15 (2003), MIT Press, Cambridge, MA 721-728
8. Boutell M.R., Luo J., Shen X., and Brown C.M. Learning multi-label scene classification. Pattern Recognition 37 9 (2004) 1757-1771
9. S. Gao, W. Wu, C.-H. Lee, T.-S. Chua, A MFoM learning approach to robust multiclass multi-label text categorization, in: Proceedings of the 21st International Conference on Machine Learning, 2004, pp. 329-336.
10. Romero E., Màrquez L., and Carreras X. Margin maximization with feed-forward neural networks: a comparative study with svm and adaboost. Neurocomputing 57 (2004) 313-344
11. Kazawa H., Izumitani T., Taira H., and Maeda E. Maximal margin labeling for multi-topic text categorization. Advances in Neural Information Processing Systems vol. 17 (2005), MIT Press, Cambridge, MA 649-656
12. Zhang M.-L., and Zhou Z.-H. Multi-label neural networks with applications to functional genomics and text categorization. IEEE Transactions on Knowledge and Data Engineering 18 10 (2006) 1338-1351
13. Zhang M.-L., and Zhou Z.-H. ML-KNN: a lazy learning approach to multi-label learning. Pattern Recognition 40 7 (2007) 2038-2048
14. I. Aleksander, WISARD to MAGNUS: a family of weightless virtual neural machines, in: RAM-Based Neural Networks, World Scientific, Singapore, 1998, pp. 18-30.
15. Ludermir T.B., Carvalho A.C.P.L.F., Braga A.P., and Souto M.D. Weightless neural models: a review of current and past works. Neural Computing Surveys 2 (1999) 41-61
16. A.F.D. Souza, F. Pedroni, E. Oliveira, P.M. Ciarelli, W.F. Henrique, L. Veronese, Automated free text classification of economic activities using VG-RAM weightless neural networks, in: Proceedings of the 7th IEEE International Conference on Intelligent Systems Design and Applications, IEEE Computer Society Press, Silver Spring, MD, 2007, pp. 782-787.
17. Schapire R.E., and Singer Y. Improved boosting algorithms using confidence-rated predictions. Machine Learning 27 3 (1999) 297-336
18. Aleksander I. Self-adaptive universal logic circuits. IEE Electronic Letters 2 8 (1966) 231-232
19. R.J. Mitchell, J.M. Bishop, S.K. Box, J.F. Hawker, Comparison of some methods for processing grey level data in weightless networks, in: RAM-Based Neural Networks, World Scientific, Singapore, 1998, pp. 61-70.
20. Yu C., Cui B., Wang S., and Su J. Efficient index-based knn join processing for high-dimensional data. Information and Software Technology 49 4 (2007) 332-344
21. Bueno R., Traina A.J.M., and Traina J.C. Genetic algorithms for approximate similarity queries. Data & Knowledge Engineering 62 3 (2007) 459-482
22. Komati K.S., and De Souza A.F. Using weightless neural networks for vergence control in an artificial vision system. Applied Bionics and Biomechanics 1 1 (2003) 21-31
23. I. Aleksander, C. Browne, B. Dunmall, T. Wright, Towards visual awareness in a neural system, in: Brain-Like Computing and Intelligent Information Systems, Springer, Berlin, 1997, pp. 513-533.
24. Novel Technical Solutions, Neural Representation Modeller 〈http://www.ntsu.com〉, 2007.
25. CNAE, Classificação Nacional de Atividades Econômicas-Fiscal (CNAE-Fiscal) 1.1, Technical Report, Instituto Brasileiro de Geografia e Estatística (IBGE), Rio de Janeiro, RJ (2003).
26. S.T. Dumais, J. Platt, D. Heckerman, M. Sahami, Inductive learning algorithms and representation for text categorization, in: Proceedings of the 7th ACM International Conference on Information and Knowledge Management, 1998, pp. 148-155.
27. Carneiro R., Dias S.S., Fardin Jr. D., Oliveira S., Garcez A.S.d., and De Souza A.F. Improving VG-RAM neural networks performance using knowledge correlation. Lecture Notes in Computer Science vol. 4232 (2006), Springer, Berlin 427-436