A framework to generate synthetic multi-label datasets

Electronic Notes in Theoretical Computer Science

Volumn 302, Issue , 2014, Pages 155-176

  Tomás, Jimena Torres ^a   Spolaôr, Newton ^a   Cherman, Everton Alvares ^a   Monard, Maria Carolina ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

artificial datasets; data generator; Java; multi label learning; PHP; publicly available framework

Indexed keywords

EID: 84894202914     PISSN: 15710661     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.entcs.2014.01.025     Document Type: Article

References (18)

1. R. Agrawal, S. Ghosh, T. Imielinski, B. Iyer, and A. Swami An interval classifier for database mining applications International Conference on Very Large Databases 1992 560 573
2. V. Bolón-Canedo, N. Sánchez-Maroño, and A. Alonso-Betanzos A review of feature selection methods on synthetic data Knowledge and Information Systems 34 2013 483 519
3. S. Chou, and C.-L. Hsu MMDT: a multi-valued and multi-labeled decision tree classifier for data mining Expert Systems with Applications 28 2005 799 812
4. A. Clare, and R.D. King Knowledge discovery in multi-label phenotype data European Conference on Principles of Data Mining and Knowledge Discovery 2001 42 53
5. S. Dendamrongvit, P. Vateekul, and M. Kubat Irrelevant attributes and imbalanced classes in multi-label text-categorization domains Intelligent Data Analysis 15 2011 843 859
6. T.G. Dietterich Exploratory research in machine learning Machine Learning 5 1990 5 10
7. D.M. dos Reis, E.A. Cherman, N. Spolaôr, and M.C. Monard Extensions of the multi-label learning algorithm BRkNN (in Portuguese) Encontro Nacional de Inteligência Artificial 2012 1 12
8. T. Hastie, R. Tibshirani, and J. Friedman The Elements of Statistical Learning 2001 Springer-Verlag
9. P. Langley Crafting papers on machine learning International Conference on Machine Learning 2000 1207 1212
10. H. Liu, and H. Motoda Computational Methods of Feature Selection 2008 Chapman & Hall/CRC
11. H.G. Noh, M.S. Song, and S.H. Park An unbiased method for constructing multilabel classification trees Computational Statistics & Data Analysis 47 2004 149 164
12. D. Richeson Volumes of n-dimensional balls URL http://divisbyzero.com/ 2010/05/09/volumes-of-n-dimensional-balls 2010
13. E. Spyromitros, G. Tsoumakas, and I. Vlahavas An empirical study of lazy multilabel classification algorithms Hellenic conference on Artificial Intelligence 2008 401 406
14. Tsoumakas, G.; Personal communication (2013).
15. G. Tsoumakas, I. Katakis, and I.P. Vlahavas Mining multi-label data O. Maimon, L. Rokach, Data Mining and Knowledge Discovery Handbook 2010 Springer 667 685
16. G. Tsoumakas, E.S. Xioufis, J. Vilcek, and I.P. Vlahavas Mulan: A java library for multi-label learning Journal of Machine Learning Research 12 2011 2411 2414
17. Z. Younes, F. Abdallah, T. Denoeux, and H. Snoussi A dependent multilabel classification method derived from the k-nearest neighbor rule EURASIP Journal on Advances in Signal Processing 2011 2011 1 14
18. M.-L. Zhang, J.M. Peña, and V. Robles Feature selection for multi-label naive bayes classification Information Sciences 179 2009 3218 3229