Classifier ensembles: Select real-world applications

By Nikunj C. Oza, Kagan Tumer. Information Fusion, 2008

327 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Challenges in discriminating profanity from hate speech (2017, result): “They have been applied to a wide range of real-world problems and shown to achieve better results compared to single-classiﬁer methods (Oza and Tumer 2008).”
Fusion Techniques for Reliable Information: A Survey (2010, background): “EM should examine different applications that lead to these modern data mining problems and how novel ensemble methods aid in solving these problems [72], [89].”
A survey of multiple classifier systems as hybrid systems (2014, background): “A recent review appears in [18].”
Ensemble learning: A survey (2018, background): “Oza and Tumer (2008) reviewed application of ensemble methods in remote sensing, person recognition, one versus all recognition, and medicine.”
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches (2012, background): “led to significant improvements [27].”