Increasing Performance of Supervised Machine Learning Methods by Analysis of Construction and Demolition Waste |
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| Petr Kuritcyn, Katharina Anding, Gunther Notni |
- Abstract:
- Any recognition task, where the classes are given by quality rules or standards, needs the use of supervised machine learning. This paper discusses the ways of improvement the performance of methods of spectral analysis and supervised machine learning by classifying the construction and demolition waste (CDW). The first investigations in visible (VIS) and infrared (IR) spectrum have shown, that we can achieve a high recognition rate (98.3%). Therefore, investigations were done for analysing, which methods are useful for improvement classification performance of C&D aggregates.
- Download:
- IMEKO-TC10-2016-067.pdf
- DOI:
- -
- Event details
- IMEKO TC:
- TC10
- Event name:
- TC10 Workshop on Technical Diagnostics 2016
- Title:
14th IMEKO TC10 Workshop “New Perspectives in Measurements, Tools and Techniques for system’s reliability, maintainability and safety”
- Place:
- Milano, ITALY
- Time:
- 27 June 2016 - 28 June 2016