Ensemble Learning

Ensemble Learning. Polikar, R. In Zhang, C. & Ma, Y., editors, Ensemble Machine Learning: Methods and Applications, pages 1–34. Springer US, Boston, MA, 2012.

Paper doi abstract bibtex

Over the last couple of decades, multiple classifier systems, also called ensemble systems have enjoyed growing attention within the computational intelligence and machine learning community. This attention has been well deserved, as ensemble systems have proven themselves to be very effective and extremely versatile in a broad spectrum of problem domains and real-world applications. Originally developed to reduce the variance—thereby improving the accuracy—of an automated decision-making system, ensemble systems have since been successfully used to address a variety of machine learning problems, such as feature selection, confidence estimation, missing feature, incremental learning, error correction, class-imbalanced data, learning concept drift from nonstationary distributions, among others. This chapter provides an overview of ensemble systems, their properties, and how they can be applied to such a wide spectrum of applications.

@incollection{polikar_ensemble_2012,
	address = {Boston, MA},
	title = {Ensemble {Learning}},
	isbn = {978-1-4419-9326-7},
	url = {https://doi.org/10.1007/978-1-4419-9326-7_1},
	abstract = {Over the last couple of decades, multiple classifier systems, also called ensemble systems have enjoyed growing attention within the computational intelligence and machine learning community. This attention has been well deserved, as ensemble systems have proven themselves to be very effective and extremely versatile in a broad spectrum of problem domains and real-world applications. Originally developed to reduce the variance—thereby improving the accuracy—of an automated decision-making system, ensemble systems have since been successfully used to address a variety of machine learning problems, such as feature selection, confidence estimation, missing feature, incremental learning, error correction, class-imbalanced data, learning concept drift from nonstationary distributions, among others. This chapter provides an overview of ensemble systems, their properties, and how they can be applied to such a wide spectrum of applications.},
	language = {en},
	urldate = {2021-03-25},
	booktitle = {Ensemble {Machine} {Learning}: {Methods} and {Applications}},
	publisher = {Springer US},
	author = {Polikar, Robi},
	editor = {Zhang, Cha and Ma, Yunqian},
	year = {2012},
	doi = {10.1007/978-1-4419-9326-7_1},
	keywords = {Combination Rule, Concept Drift, Ensemble Member, Incremental Learning, Majority Vote},
	pages = {1--34},
}

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