AdaDT: An adaptive decision tree for addressing local class imbalance based on multiple split criteria

As it is well known, decision tree is a kind of data-driven classification model, and its primary core is the split criterion. Although a great deal of split criteria have been proposed so far, almost all of them focus on the global class distribution of the training data. However, they ignored the...

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Veröffentlicht in:	Applied intelligence (Dordrecht, Netherlands) Jg. 51; H. 7; S. 4744 - 4761
Hauptverfasser:	Yan, Jianjian, Zhang, Zhongnan, Dong, Huailin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.07.2021 Springer Nature B.V
Schlagworte:	Artificial Intelligence Computer Science Criteria Datasets Decision trees Machines Manufacturing Mechanical Engineering Processes Multiple split criteria Local class imbalance Decision tree Binary classification problem
ISSN:	0924-669X, 1573-7497
Online-Zugang:	Volltext
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