Partially Supervised Classification for Industrial System using Deep Neural Network
A general classification setting requires prior knowledge (i.e. labeled samples) to cover all classes. However, in many industrial problems, prior knowledge usually does not describe all the classes, and the generation of a complete training set that cover all classes often is a time-consuming, expensive and difficult (if not impossible) task. Our target of this work is, given labeled samples from only a subset of classes, how to assign label to any sample that potentially come from either known or unknown classes in real time data. We test our algorithm on industrial failure classification and experiments show that our method outperforms existing popular baselines.
How to Cite
partially supervised learning, classification, indsutraial, clustering
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.