Exploiting Domain Knowledge to address Multi-Class Imbalance and a Heterogeneous Feature Space in Classification Tasks for Manufacturing Data
Summary: Exploits domain knowledge to jointly tackle multi-class imbalance and heterogeneous feature space in manufacturing end-of-line classification. Domain-guided data prep yields a classifier that outperforms baselines and reduces rework on real-world quality data. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Vitali Hirsch (Daimler Truck AG)
- 2. Peter Reimann (University of Stuttgart)
- 3. Bernhard Mitschang (University of Stuttgart)
BibTeX Citation
@article{hirsch_vldb20,
title = {{Exploiting Domain Knowledge to address Multi-Class Imbalance and a Heterogeneous Feature Space in Classification Tasks for Manufacturing Data}},
author = {Hirsch, Vitali and Reimann, Peter and Mitschang, Bernhard},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {12},
pages = {3258--3271},
doi = {10.14778/3415478.3415549},
url = {https://doi.org/10.14778/3415478.3415549},
year = {2020}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,219 | Chimera: Large-Scale Classification using Machine Learning, Rules, and Crowdsourcing | 2014 | VLDB | 8.9303727e-05 |
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