Abstract
Ensemble methods such as AdaBoost are popular machine learning methods that create highly accurate classifier by combining the predictions from several classifiers. We present a parametrized method of AdaBoost that we call Top-k Parametrized Boost. We evaluate our and other popular ensemble methods from a classification perspective on several real datasets. Our empirical study shows that our method gives the minimum average error with statistical significance on the datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Maclin, R., Opitz, D.: Popular ensemble methods: An empirical study. arXiv preprint arXiv:1106.0257 (2011)
Freund, Y., Schapire, R.E.: A desicion-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P.M.B. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995)
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (2005)
Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of machine learning. MIT press (2012)
Japkowicz, N., Shah, M.: Evaluating Learning Algorithms. Cambridge University Press (2011)
Asuncion, A., Newman, D.J.: UCI machine learning repository (2007)
Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)
Kanji, G.K.: 100 statistical tests. Sage (2006)
Liaw, A., Wiener, M.: Classification and regression by randomforest. R News 2, 18–22 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Turki, T., Ihsan, M., Turki, N., Zhang, J., Roshan, U., Wei, Z. (2014). Top-k Parametrized Boost. In: Prasath, R., O’Reilly, P., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8891. Springer, Cham. https://doi.org/10.1007/978-3-319-13817-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-13817-6_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13816-9
Online ISBN: 978-3-319-13817-6
eBook Packages: Computer ScienceComputer Science (R0)