Abstract
Although machine learning has gained great success in industry, there are still many challenges in mining industrial data, especially in manufacturing domains. Because industrial data can be 1) shallow and wide, 2) highly heterogeneous and sparse. Particularly, mining on sparse data (i.e. data with missing features) is extremely challenging, because it is not easy to fill in some features (e.g. images), and removing data points would reduce the data size further. Thus, in this work, we propose a machine learning framework including transfer learning, heterogeneous feature fusion, principal component analysis and gradient boosting to solve these challenges and effectively develop predictive models on industrial datasets. Compared to a non-fusion method and a traditional fusion method on two real world datasets from Toyota Motor Corporation, the results show that the proposed method can not only maximize the utility of available features and data to achieve more stable and better performance, but also give more flexibility when predicting new unseen data points with only partial set of features available.(Code and data are available at: https://github.com/zyz293/FusionML.)
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Acknowledgment
This work was supported in part by Toyota Motor Corporation and NIST CHiMaD (70NANB19H005).
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Yang, Z. et al. (2021). Heterogeneous Feature Fusion Based Machine Learning on Shallow-Wide and Heterogeneous-Sparse Industrial Datasets. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12664. Springer, Cham. https://doi.org/10.1007/978-3-030-68799-1_41
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DOI: https://doi.org/10.1007/978-3-030-68799-1_41
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