@inproceedings{ZoghlamiKadenVillmannetal.2021,
  author    = {Feryel Zoghlami and Marika Kaden and Thomas Villmann and Germar Schneider and Harald Heinrich},
  title     = {Sensors Data Fusion for Smart Decisions Making Using Interpretative Machine Learning Models},
  series = {26. Interdisziplin{\"a}re Wissenschaftliche Konferenz Mittweida},
  number    = {002},
  publisher = {Hochschule Mittweida},
  address   = {Mittweida},
  issn      = {1437-7624},
  doi       = {10.48446/opus-12298},
  url       = {https://nbn-resolving.org/urn:nbn:de:bsz:mit1-opus4-122983},
  pages     = {145 -- 146},
  year      = {2021},
  abstract  = {Sensor fusion is an important and crucial topic in many industrial applications. One of the challenging problems is to find an appropriate sensor combination for the dedicated application or to weight their information adequately. In our contribution, we focus on the application of the sensor fusion concept together with the reference to the distance-based learning for object classification purposes. The developed machine learning model has a bi-functional architecture, which learns on the one side the discrimination of the data regarding their classes and, on the other side, the importance of the single signals, i.e., the contribution of each sensor to the decision. We show that the resulting bi-functional model is interpretative, sparse, and simple to integrate in many standard artificial neural networks.},
  language  = {en}
}