IMCStacking: Cost-sensitive stacking learning with feature inverse mapping for imbalanced problems

Stacking related methods develop rapidly recent years. However, few Stacking based ensemble methods are designed for imbalanced problems. In this paper, a novel Feature Inverse Mapping based Cost-sensitive Stacking learning (IMCStacking) is proposed to solve the problems encountered in imbalanced cl...

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Veröffentlicht in:	Knowledge-based systems 2018-06, Vol.150, p.27-37
Hauptverfasser:	Cao, Chenjie, Wang, Zhe
Format:	Artikel
Sprache:	eng
Schlagworte:	Classification Classifiers Cost analysis Cost-sensitive Decision tree ensemble Decision trees Feature extraction Feature mapping Imbalanced problems Inverse problems Linear algebra Linear classifiers Mapping Stacking
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