Year: 2018
Type: book-chapter
Abstract: This paper presents an application of a random forest based classifier that aims at recognizing flawed products in a highly automated production environment. Within the course of this paper, some data set and application features are highlighted that make the underlying classification problem rather complex and hinders the usage of machine learning algorithms straight out-of-the-box. The findings regarding these features and how to treat the concluded challenges are highlighted in a abstracted and generalized manner.
Author: Gerd Gröner
Institution: Carl Zeiss (Germany)
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2904589806