Cost-Sensitive Classification on Pathogen Species of Bacterial Meningitis by Surface Enhanced Raman Scattering

Jan, Te-Kang; Lin, Hung‐Mo; Chen, Hsin-Pai; Chern, Tsung-Chen; Huang, Chung-Yueh; Wen, Bing-Cheng; Chung, Chia-Wen; Li, Yung-Jui; Chuang, Ya-Ching; Li, Lili; Chan, Yu-Jiun; Wang, Juen-Kai; Wang, Yuh‐Lin; Lin, Chi‐Hung; Wang, Dawei

doi:10.1109/bibm.2011.133

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…Nevertheless, using accuracy as the evaluation metric for learning does not always produce the most useful classification system in the real world. In fact, many real-world applications [12,13,14,15,16], including vision related, demand varying costs for different types of misclassification errors. For example, different costs are useful for building a realistic face recognition system [15,17,18,19], in which a government staff being misrecognized as an impostor causes only a slight inconvenience; however, an imposer misrecognized as a staff can result in serious damage.…”

Section: Introductionmentioning

confidence: 99%

Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation

Chung

Yang

Lin

2020

2020 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)

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While deep neural networks have succeeded in several visual applications, such as object recognition, detection, and localization, by reaching very high classification accuracies, it is important to note that many real-world applications demand varying costs for different types of misclassification errors, thus requiring cost-sensitive classification algorithms. Current models of deep neural networks for cost-sensitive classification are restricted to some specific network structures and limited depth. In this paper, we propose a novel framework that can be applied to deep neural networks with any structure to facilitate their learning of meaningful representations for cost-sensitive classification problems. Furthermore, the framework allows endto-end training of deeper networks directly. The framework is designed by augmenting auxiliary neurons to the output of each hidden layer for layer-wise cost estimation, and including the total estimation loss within the optimization objective. Experimental results on public benchmark visual data sets with two cost information settings demonstrate that the proposed framework outperforms state-of-the-art cost-sensitive deep learning models.

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Section: Introductionmentioning

confidence: 99%

Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation

Chung

Yang

Lin

2020

2020 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)

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A simple methodology for soft cost-sensitive classification

Jan

Wang

Lin³

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Many real-world data mining applications need varying cost for different types of classification errors and thus call for cost-sensitive classification algorithms. Existing algorithms for cost-sensitive classification are successful in terms of minimizing the cost, but can result in a high error rate as the trade-off. The high error rate holds back the practical use of those algorithms. In this paper, we propose a novel costsensitive classification methodology that takes both the cost and the error rate into account. The methodology, called soft cost-sensitive classification, is established from a multicriteria optimization problem of the cost and the error rate, and can be viewed as regularizing cost-sensitive classification with the error rate. The simple methodology allows immediate improvements of existing cost-sensitive classification algorithms. Experiments on the benchmark and the real-world data sets show that our proposed methodology indeed achieves lower test error rates and similar (sometimes lower) test costs than existing cost-sensitive classification algorithms.

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Advances in Cost-sensitive Multiclass and Multilabel Classification

Lin

2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Cost-Sensitive Classification on Pathogen Species of Bacterial Meningitis by Surface Enhanced Raman Scattering

Cited by 5 publications

References 14 publications

Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation

Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation

A simple methodology for soft cost-sensitive classification

Advances in Cost-sensitive Multiclass and Multilabel Classification

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