Abstract
The paper presents an approach to the task of a data mining competition “Trading Based on Recommendations”. The approach is based on a heterogeneous ensemble of classification models, where each model was created independently by different participant of the competition. Each base-model is presented in the paper and a concept, and results of the overall solution are discussed.
B. Szwej—The authors would like to thank all the participants develo** the models included in the presented ensemble: Karolina Adamczyk, Szymon Bartnik, Michał Bychawski, Piotr Dankowski, Tomasz Dabek, Piotr Drewniak, Michał Dziwoki, Łukasz Gawin, Krzysztof Hanzel, Karol Herok, Karol Kalaga, Mateusz Kaleta, Grzegorz Kozłowski, Robert Krupa, Mateusz Łysień, Mateusz Małota, Wojciech Niemkowski, Krzysztof Paszek, Stefania Perlak, Dawid Poloczek, Marek Pownug, Tomasz Rzepka, Artur Siedlecki, Krzysztof Śniegoń, Katarzyna Toporek.
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
Similar content being viewed by others
References
Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996). http://dx.doi.org/10.1007/BF00058655
Chang, P.-C., Liu, C.-H., Fan, C.-Y., Lin, J.-L., Lai, C.-M.: An ensemble of neural networks for stock trading decision making. In: Huang, D.-S., Jo, K.-H., Lee, H.-H., Kang, H.-J., Bevilacqua, V. (eds.) ICIC 2009. LNCS, vol. 5755, pp. 1–10. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04020-7_1
Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000). doi:10.1007/3-540-45014-9_1
Freund, Y., Schapire, R.E.: A desicion-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995). doi:10.1007/3-540-59119-2_166
Janusz, A., Ślezak, D., Stawicki, S., Rosiak, M.: Knowledge pit-a data challenge platform. In: CS&P, pp. 191–195 (2015)
Kong, E.B., Dietterich, T.G.: Error-correcting output coding corrects bias and variance. In: Proceedings of the Twelfth International Conference on Machine Learning (1995)
Mendes-Moreira, J., Soares, C., Jorge, A.M., Sousa, J.F.D.: Ensemble approaches for regression: a survey. ACM Comput. Surv. 45(1), 10:1–10:40. http://doi.acm.org/10.1145/2379776.2379786
Opitz, D., Maclin, R.: Popular ensemble methods: an empirical study. J. Artif. Intell. Res. 11, 169–198 (1999)
Seker, S.E., Mert, C., Al-Naami, K., Ayan, U., Ozalp, N.: Ensemble classification over stock market time series and economy news. In: 2013 IEEE International Conference on Intelligence and Security Informatics, pp. 272–273, June 2013
Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington (2011)
Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241–259 (1992). http://www.sciencedirect.com/science/article/pii/S0893608005800231
Acknowledgements
The work was carried out within the statutory research project of the Institute of Electronics, Silesian University of Technology: BK_220/RAu-3/2016 (02/030/BK_16/0017).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Kozielski, M., Dusza, K., Flakus, J., Kozłowski, K., Musiał, S., Szwej, B. (2017). Heterogeneous Ensemble of Specialised Models - A Case Study in Stock Market Recommendations. In: Kryszkiewicz, M., Appice, A., Ślęzak, D., Rybinski, H., Skowron, A., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2017. Lecture Notes in Computer Science(), vol 10352. Springer, Cham. https://doi.org/10.1007/978-3-319-60438-1_71
Download citation
DOI: https://doi.org/10.1007/978-3-319-60438-1_71
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60437-4
Online ISBN: 978-3-319-60438-1
eBook Packages: Computer ScienceComputer Science (R0)