---
title: "Cost-Aware Pre-Training for Multiclass Cost-Sensitive Deep Learning"
authors:
  - "Yu-An Chung"
  - "Hsuan-Tien Lin"
  - "Shao-Wen Yang"
doi: ""
url: "https://ijcai.org/proceedings/2016/203"
pdf: "https://ijcai.org/proceedings/2016/0203.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "1411-1416"
track: ""
keywords:
---

# Cost-Aware Pre-Training for Multiclass Cost-Sensitive Deep Learning

Yu-An Chung, Hsuan-Tien Lin, Shao-Wen Yang

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 1411-1416.

## Abstract

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 first design a novel loss function that embeds the cost information for the training stage of cost-sensitive deep learning. We then show that the loss function can also be integrated into the pre-training stage to conduct cost-aware feature extraction more effectively. Extensive experimental results justify the validity of the novel loss function for making existing deep learning models cost-sensitive, and demonstrate that our proposed model with cost-aware pre-training and training outperforms non-deep models and other deep models that digest the cost information in other stages.