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Abstract: Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. It was shown theoretically and experimentally that in order for an ensemble to be effective, it should consist of high-accuracy base classifiers that should have high diversity in their predictions. One technique, which proved to be effective for constructing an ensemble of accurate and... (Update)
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BibTeX entry: (Update)
A. Tsymbal, M. Pechenizkiy, and P. Cunningham. Diversity in ensemble feature selection. Technical report, Trinity College Dublin, 2003. http://www.cs.tcd.ie/publications/tech-reports/reports.03/TCD-CS-2003-44.pdf. http://citeseer.ist.psu.edu/tsymbal03diversity.html More
@misc{ tsymbal03diversity,
author = "A. Tsymbal and M. Pechenizkiy and P. Cunningham",
title = "Diversity in ensemble feature selection",
text = "A. Tsymbal, M. Pechenizkiy, and P. Cunningham. Diversity in ensemble feature
selection. Technical report, Trinity College Dublin, 2003. http://www.cs.tcd.ie/publications/tech-reports/reports.03/TCD-CS-2003-44.pdf.",
year = "2003",
url = "citeseer.ist.psu.edu/tsymbal03diversity.html" }
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