New feature splitting criteria for co-training using genetic algorithm optimization

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5997 LNCS, Issue , 2010, Pages 22-32

  Salaheldin, Ahmed ^a   El Gayar, Neamat ^a,b  

^a NILE UNIVERSITY   (Egypt)

^b Cairo University   (Egypt)

Author keywords

[No Author keywords available]

Indexed keywords

GENETIC ALGORITHMS; LABELED DATA; SEMI-SUPERVISED LEARNING;

CLASSIFICATION MODELS; CO-TRAINING; GENETIC-ALGORITHM OPTIMIZATIONS; KEY FEATURE; MIXED CRITERIA; SEMI-SUPERVISED LEARNING TECHNIQUES; SPLITTING CRITERION; UNLABELED DATA;

LEARNING SYSTEMS;

EID: 77952086656     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-12127-2_3     Document Type: Conference Paper

References (12)

1. Zhu, X.: Semi-supervised learning literature survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison (2005)
2. Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the eleventh annual conference on Computational Learning Theory (COLT 1998), pp. 92-100 (1998)
3. Nigam, K., Ghani, R.: Analyzing the effectiveness and applicability of co-training. In: Proceedings of the Ninth International Conference on Information and Knowledge (CIKM 2000), pp. 86-93 (2000)
4. Feger, F., Koprinska, I.: Co-training using rbf nets and different feature splits. In: Proceedings of 2006 International Joint Conference on Neural Network, pp. 1878-1885 (2006)
5. Wang, W., Zhou, Z.-H.: Analyzing co-training style algorithms. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 454-465. Springer, Heidelberg (2007)
6. Opitz, D.: Feature selection for ensembles. In: Proceedings of the 16th International Conference on Artificial Intelligence, pp. 379-384 (1999)
7. Goldberg, D.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading (1989)
8. Kamp, R.G., Savenije, H.H.G.: Optimising training data for anns with genetic algorithms. In: Hydrol. Earth Syst. Sci., pp. 603-608 (2006)
9. Asuncion, A., Newman, D.J.: Uci machine learning repository (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
10. Rebecca Fay, F.S., Kaufmann, U., Palm, G.: Learning object recognition in a neurobotic system. In: Groß, H.-M., Debes, K., Böhme, H.-J. (eds.) 3rd Workshop on SelfOrganization of AdaptiVE Behavior, SOAVE 2004, pp. 198-209 (2004)
11. Kiritchenko, S., Matwin, S.: Email classification with co-training. In: Proceedings of CASCON 2001, Toronto, Canada, pp. 192-201 (2001)
12. Terabe, M., Hashimoto, K.: Evaluation criteria of feature splits for co-training. In: Proceedings of the International MultiConference of Engineers and Computer Scientists 2008 (2008)