Abstract
Machine Learning is a field in which significant steps forward have been taken in the last years, resulting in a wide variety of available algorithms, for many different problems. Nonetheless, most of these algorithms focus on the training of static models, in the sense that the model stops evolving after the training phase. This is increasingly becoming a limitation, especially in an era in which datasets are increasingly larger and may even arrive as sequential streams of data. Frequently retraining a model, in these scenarios, is not realistic. In this paper we propose evoRF: a combination of a Random Forest with an evolutionary approach. Its key innovative aspect is the evolution of the weights of the Random Forest over time, as new data arrives, thus making the forest’s voting scheme adapt to the new data. Older trees can also be replaced by newly trained ones, according to their accuracy, ensuring that the ensemble remains up to date without requiring a whole retraining.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern. 21(3), 660–674 (1991)
Janikow, C.Z.: Fuzzy decision trees: issues and methods. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 28(1), 1–14 (1998)
Singh, A., Thakur, N., Sharma, A.: A review of supervised machine learning algorithms. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 1310–1315. IEEE (2016)
Gehrke, J., Ramakrishnan, R., Ganti, V.: Rainforest-a framework for fast decision tree construction of large datasets. VLDB 98, 416–427 (1998)
Ranka, S., Singh, V.: Clouds: a decision tree classifier for large datasets. In: Proceedings of the 4th Knowledge Discovery and Data Mining Conference, vol. 2, p. 8 (1998)
Vilalta, R., Drissi, Y.: A perspective view and survey of meta-learning. Artif. Intell. Rev. 18(2), 77–95 (2002)
Biau, G., Scornet, E.: A random forest guided tour. Test 25(2), 197–227 (2016)
Acknowledgments
This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ramos, D., Carneiro, D., Novais, P. (2020). evoRF: An Evolutionary Approach to Random Forests. In: Kotenko, I., Badica, C., Desnitsky, V., El Baz, D., Ivanovic, M. (eds) Intelligent Distributed Computing XIII. IDC 2019. Studies in Computational Intelligence, vol 868. Springer, Cham. https://doi.org/10.1007/978-3-030-32258-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-32258-8_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32257-1
Online ISBN: 978-3-030-32258-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)