Empirical Comparative Study of Boosting and its Relatives

Abstract:

Transfer learning is an important research topic in machine learning and data mining that focuses on utilizing knowledge and skills learned in previous tasks to a novel but related task. This paper contributes to comparison between boosting for transfer learning and boosting. The results, in terms of the accuracy, weighted F-Measure, G-Mean, weighted GMPR, weighted precision and weighted AUC, are rigorously tested using the statistical framework proposed by Janez Demsar. Results show that the performance difference between TrAdaBoost and AdaBoost is less significant.

• Periodical: Applied Mechanics and Materials (Volume 312)
• Pages: 667-672
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.312.667
• Online since: February 2013
• Authors: Fang Jun Wu
• Keywords: Boosting, Classification, Transfer Learning

References

[1] Sinno Jialin Pan, Qiang Yang, A survey on transfer learning, IEEE Transactions on Knowledge and Data Engineering. 22, 10(2010)1345-1359.

[2] Yongqiang Liu, A review about transfer learning methods and applications, In 2011 International Conference on Information and Network Technology. (2011) 7-11.

[3] Lisa Torrey, Jude Shavlik, Transfer learning. In: E. Soria, J. Martin, R. Magdalena, M. Martinez and A.Serrano (Eds.), Handbook of Research on Machine Learning Applications, 2009.

[4] Wenyuan Dai, Qiang Yang, Gui-Rong Xue, Yong Yu, Boosting for transfer learning, In Proceedings of the 24th Annual International Conference on Machine Learning. (2007) 193-200.

DOI: 10.1145/1273496.1273521

[5] Yoav Freund, Robert Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences. 55, 1(1997) 119-139.

DOI: 10.1006/jcss.1997.1504

[6] http://www.cse.ust.hk/TL/

[7] http://www.cs.waikato.ac.nz/ml/weka/

[8] Joshi M.V., On evaluating performance of classifiers for rare classes, In Proceedings of the 2nd International Conference on Data Mining. (2002) 641-644.

[9] Yanmin Sun, Mohamed S. Kamel, Yang Wang, Boosting for learning multiple classes with imbalanced class distribution, In Proceedings of the 6th International Conference on Data Mining. (2006) 592-602.

DOI: 10.1109/icdm.2006.29

[10] Jin Huang, Ling C.X., Using AUC and accuracy in evaluating learning algorithms, IEEE Transactions on Knowledge and Data Engineering. 17, 3(2005)299-310.

DOI: 10.1109/tkde.2005.50