2015/11/30 by Yu-An Chung, Hsuan-Tien Lin, Chung, Yu-An +3
Computer Science · Engineering · #FOS: Computer and information sciences #Industrial Vision Systems and Defect Detection #Infrastructure Maintenance and Monitoring #Machine Learning (cs.LG) #Machine Learning and Data Classification #Neural and Evolutionary Computing (cs.NE)
paper · pdf · doi:10.48550/arxiv.1511.09337
openalex publication_date 2015/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.