Evaluation of Feature Selection and Pre-Processing Techniques for Ethylene Glycol-Water Ratio Classification in Process Thermostat
- With the focus in the realm of automotive testing, process thermostats play a vital role in providing the required operating environment. These process thermostats, with the operating medium of an ethylene glycol-water ratio, play a crucial role in terms of controlling their thermal properties. With an emphasis on identifying the best preprocessing method for classifying these ratios, this study utilises a detailed comparison of statistical methods and the wrapper method-based Genetic Algorithm as a search method for the most relevant feature selections. Given the huge number of existing sensor parameters in the system, thereby emphasising the importance of feature selection criteria for effective analysis and model training. Furthermore, a random forest-based classifier is used with these parameters to predict the accurate ethylene glycol to water ratio.
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| Author: | Patrick Harfmann, Akash Mangaluru RamanandaORCiD, Fabian Wagner, Vishnu Murali, Sharath Babu Lokesh, Magnus Nigmann, Markus KleyORCiD |
|---|---|
| URN: | urn:nbn:de:bsz:944-opus4-33569 |
| DOI: | https://doi.org/10.1016/j.procs.2024.09.588 |
| Source Title (English): | International Conference on Knowledge-Based and Intelligent Information & Engineering Systems |
| Conference Name: | KES |
| Conference Date: | 11-13 September |
| Conference Place: | Seville, Spain |
| Document Type: | Conference Proceeding |
| Language: | English |
| Year of Completion: | 2024 |
| Release Date: | 2024/12/02 |
| Number of Pages: | 10 |
| First Page: | 1446 |
| Last Page: | 1455 |
| Faculty: | Maschinenbau und Werkstofftechnik |
| Open Access: | Open Access |
| Licence (German): |  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |