# Cost-aware Pre-training for Multiclass Cost-sensitive Deep Learning

arXiv: https://arxiv.org/abs/1511.09337 · HTML: https://mlsanity.com/paper/1511.09337/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2015.

## Built on by (most cited first, 9 of 9)

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- [Rethinking Cost-sensitive Classification in Deep Learning via Adversarial Data Augmentation](https://mlsanity.com/paper/2208.11739/) (2022)
- [A LSTM and Cost-Sensitive Learning-Based Real-Time Warning for Civil Aviation Over-limit](https://mlsanity.com/paper/2305.04618/) (2023)

## Abstract (arXiv)

Deep learning has been one of the most prominent machine learning techniques nowadays, being the state-of-the-art on a broad range of applications where automatic feature extraction is needed. Many such applications also demand varying costs for different types of mis-classification errors, but it is not clear whether or how such cost information can be incorporated into deep learning to improve performance. In this work, we propose a novel cost-aware algorithm that takes into account the cost information into not only the training stage but also the pre-training stage of deep learning. The approach allows deep learning to conduct automatic feature extraction with the cost information effectively. Extensive experimental results demonstrate that the proposed approach outperforms other deep learning models that do not digest the cost information in the pre-training stage.