A survey on ensemble learning

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

8 Scopus Citations
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Original languageEnglish
Pages (from-to)241–258
Journal / PublicationFrontiers of Computer Science
Issue number2
Online published30 Aug 2019
Publication statusPublished - 20 Apr 2020


Despite significant successes achieved in knowledge discovery, traditional machine learning methods may fail to obtain satisfactory performances when dealing with complex data, such as imbalanced, high-dimensional, noisy data, etc. The reason behind is that it is difficult for these methods to capture multiple characteristics and underlying structure of data. In this context, it becomes an important topic in the data mining field that how to effectively construct an efficient knowledge discovery and mining model. Ensemble learning, as one research hot spot, aims to integrate data fusion, data modeling, and data mining into a unified framework. Specifically, ensemble learning firstly extracts a set of features with a variety of transformations. Based on these learned features, multiple learning algorithms are utilized to produce weak predictive results. Finally, ensemble learning fuses the informative knowledge from the above results obtained to achieve knowledge discovery and better predictive performance via voting schemes in an adaptive way. In this paper, we review the research progress of the mainstream approaches of ensemble learning and classify them based on different characteristics. In addition, we present challenges and possible research directions for each mainstream approach of ensemble learning, and we also give an extra introduction for the combination of ensemble learning with other machine learning hot spots such as deep learning, reinforcement learning, etc.

Research Area(s)

  • clustering ensemble, ensemble learning, semi-supervised clustering ensemble, semi-supervised ensemble classification, supervised ensemble classification

Citation Format(s)

A survey on ensemble learning. / DONG, Xibin; YU, Zhiwen; CAO, Wenming; SHI, Yifan; MA, Qianli.

In: Frontiers of Computer Science, Vol. 14, No. 2, 20.04.2020, p. 241–258.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal