# A Cost-Sensitive Deep Belief Network for Imbalanced Classification

arXiv: https://arxiv.org/abs/1804.10801 · HTML: https://mlsanity.com/paper/1804.10801/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2018.

## Built on by (most cited first, 12 of 12)

- [DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data](https://mlsanity.com/paper/2105.02340/) (2021)
- [RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data](https://mlsanity.com/paper/2103.00221/) (2021)
- [Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning](https://mlsanity.com/paper/2207.06084/) (2022)
- [A Comprehensive Survey on Imbalanced Data Learning](https://mlsanity.com/paper/2502.08960/) (2025)
- [Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization](https://mlsanity.com/paper/2204.10231/) (2022)
- [Under-bagging Nearest Neighbors for Imbalanced Classification](https://mlsanity.com/paper/2109.00531/) (2021)
- [Investigating Group Distributionally Robust Optimization for Deep Imbalanced Learning: A Case Study of Binary Tabular Data Classification](https://mlsanity.com/paper/2303.02505/) (2023)
- [OWAdapt: An adaptive loss function for deep learning using OWA operators](https://mlsanity.com/paper/2305.19443/) (2023)
- [Effective Intrusion Detection in Highly Imbalanced IoT Networks with Lightweight S2CGAN-IDS](https://mlsanity.com/paper/2306.03707/) (2023)
- [Adaptive Cost-Sensitive Learning in Neural Networks for Misclassification Cost Problems](https://mlsanity.com/paper/2111.07382/) (2021)
- [INGB: Informed Nonlinear Granular Ball Oversampling Framework for Noisy Imbalanced Classification](https://mlsanity.com/paper/2307.01224/) (2023)
- [Long-Tailed Data Classification by Increasing and Decreasing Neurons During Training](https://mlsanity.com/paper/2507.09940/) (2025)

## Abstract (arXiv)

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. To deal with this problem, cost-sensitive approaches assign different misclassification costs for different classes without disrupting the true data sample distributions. However, due to lack of prior knowledge, the misclassification costs are usually unknown and hard to choose in practice. Moreover, it has not been well studied as to how cost-sensitive learning could improve DBN performance on imbalanced data problems. This paper proposes an evolutionary cost-sensitive deep belief network (ECS-DBN) for imbalanced classification. ECS-DBN uses adaptive differential evolution to optimize the misclassification costs based on training data, that presents an effective approach to incorporating the evaluation measure (i.e. G-mean) into the objective function. We first optimize the misclassification costs, then apply them to deep belief network. Adaptive differential evolution optimization is implemented as the optimization algorithm that automatically updates its corresponding parameters without the need of prior domain knowledge. The experiments have shown that the proposed approach consistently outperforms the state-of-the-art on both benchmark datasets and real-world dataset for fault diagnosis in tool condition monitoring.