A Cost-Sensitive Deep Belief Network for Imbalanced Classification

Imbalanced data with a skewed class distribution are common in many real-world applications. Deep Belief Network (DBN) is a machine learning technique that is effective in classification tasks. However, conventional DBN does not work well for imbalanced data classification because it assumes equal c...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2019-01, Vol.30 (1), p.109-122
Hauptverfasser:	Zhang, Chong, Tan, Kay Chen, Li, Haizhou, Hong, Geok Soon
Format:	Artikel
Sprache:	eng
Schlagworte:	Belief networks Classification Condition monitoring Cost sensitive Costs deep belief network Disruption Evolution evolutionary algorithm (EA) Fault diagnosis imbalanced classification Learning algorithms Learning systems Machine learning Neural networks Objective function Optimization Skewed distributions State of the art Task analysis
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