Author
Şensoy, M., Saleki, Maryam, Julier, S., Aydoğan, Reyhan, Reid, J.
Publication Date
2020
Publication Place
-
The ACM Digital Library
Subject
Cost-sensitive learning, Deep learning, Risk, Uncertainty
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
978-145037518-4
Record ID
5f32a648-8b02-4e02-beb0-ef4ce491f1a3
Library Location
Computer Science
Date
2020
Sample Text
In many tasks, classifiers play a fundamental role in the way an agent behaves. Most rational agents collect sensor data from the environment, classify it, and act based on that classification. Recently, deep neural networks (DNNs) have become the dominant approach to develop classifiers due to their excellent performance. When training and evaluating the performance of DNNs, it is normally assumed that the cost of all misclassification errors are equal. However, this is unlikely to be true in practice. Incorrect classification predictions can cause an agent to take inappropriate actions. The costs of these actions can be asymmetric, vary from agent-to-agent, and depend on context. In this paper, we discuss the importance of considering risk and uncertainty quantification together to reduce agents' cost of making misclassifications using deep classifiers.
Cilt
2020
