Evaluating ReliefF-based multi-label feature selection algorithm

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

Volumn 8864, Issue , 2014, Pages 194-205

  Spolaôr, Newton ^a   Monard, Maria Carolina ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

Feature ranking; Feature selection; Filter approach; Information Gain; Multi label learning; Systematic review

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; FEATURE EXTRACTION; LEARNING SYSTEMS;

FEATURE RANKING; FILTER APPROACH; INFORMATION GAIN; MULTI-LABEL LEARNING; SYSTEMATIC REVIEW;

LEARNING ALGORITHMS;

EID: 84921832467     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-12027-0_16     Document Type: Article

References (18)

1. Cherman, E. A., Metz, J., Monard, M. C.: Incorporating label dependency into the binary relevance framework for multi-label classification. Expert Systems with Applications 39(2), 1647-1655 (2012).
2. Demšar, J.: Statistical comparison of classifiers over multiple data sets. Journal of Machine Learning Research 7(1), 1-30 (2006).
3. Demšar, J.: Algorithms for subsetting attribute values with Relief. Machine Learning 78, 421-428 (2010).
4. Fayyad, U. M., Irani, K. B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: International Joint Conference on Artificial Intelligence, pp. 1022-1029 (1993).
5. Kitchenham, B. A., Charters, S.: Guidelines for performing systematic literature reviews in software engineering. EBSE-2007-01 Technical Report. 65 pg. Evidencebased Software Engineering (2007).
6. Kong, D., Ding, C., Huang, H., Zhao, H.: Multi-label ReliefF and F-statistic feature selections for image annotation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2352-2359 (2012).
7. Liu, H., Motoda, H.: Computational Methods of Feature Selection. Chapman & Hall/CRC (2008).
8. Marques, G., Domingues, M. A., Langlois, T., Gouyon, F.: Three current issues in music autotagging. In: Conference of the International Society for Music Information Retrieval, pp. 795-800 (2011).
9. Metz, J., de Abreu, L. F. D., Cherman, E. A., Monard, M. C.: On the estimation of predictive evaluation measure baselines for multi-label learning. In: Pavón, J., Duque-Méndez, N. D., Fuentes-Fernández, R. (eds.) IBERAMIA 2012. LNCS, vol. 7637, pp. 189-198. Springer, Heidelberg (2012).
10. Pupo, O. G. R., Morell, C., Soto, S. V.: ReliefF-ML: An extension of reliefF algorithm to multi-label learning. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds.) CIARP 2013, Part II. LNCS, vol. 8259, pp. 528-535. Springer, Heidelberg (2013).
11. Robnik-Šikonja, M., Kononenko, I.: Theoretical and empirical analysis of ReliefF and RReliefF. Machine Learning 53(1-2), 23-69 (2003).
12. Slavkov, I., Karcheska, J., Kocev, D., Kalajdziski, S., Džeroski, S.: Extending reliefF for hierarchical multi-label classification. In: Workshop on New Frontiers in Mining Complex Patterns-European Conference on Machine Learning/Principles and Practice of Knowledge Discovery in Databases, pp. 156-167 (2013).
13. Spolâor, N., Monard, M. C., Lee, H. D.: A systematic review to identify feature selection publications in multi-labeled data. ICMC Technical Report No 374. 31 pg., University of São Paulo (2012).
14. Spolâor, N., Cherman, E. A., Monard, M. C., Lee, H. D.: A comparison of multi-label feature selection methods using the problem transformation approach. Electronic Notes in Theoretical Computer Science 292, 135-151 (2013).
15. Spolâor, N., Cherman, E. A., Monard, M. C., Lee, H. D.: ReliefF for multi-label feature selection. In: Brazilian Conference on Intelligent Systems, pp. 6-11 (2013).
16. Spyromitros, E., Tsoumakas, G., Vlahavas, I. P.: An empirical study of lazy multilabel classification algorithms. In: Darzentas, J., Vouros, G. A., Vosinakis, S., Arnellos, A. (eds.) SETN 2008. LNCS (LNAI), vol. 5138, pp. 401-406. Springer, Heidelberg (2008).
17. Tsoumakas, G., Katakis, I., Vlahavas, I. P.: Mining multi-label data. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 667-685. Springer (2010).
18. Zhang, M. L., Zhou, Z. H.: A review on multi-label learning algorithms. IEEE Transactions on Knowledge and Data Engineering 99, 1-59 (2013).