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 costs for each class.

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Chong Zhang, Kay Chen Tan, Haizhou Li, Geok Soon Hong (2024). Dataset: A Cost-Sensitive Deep Belief Network for Imbalanced Classification. https://doi.org/10.57702/5n92a5as

DOI retrieved: December 2, 2024

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Created December 2, 2024
Last update December 2, 2024
Defined In https://doi.org/10.48550/arXiv.1804.10801
Author Chong Zhang
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Kay Chen Tan
Haizhou Li
Geok Soon Hong