We present a new self-organizing algorithm for classification of a data that combines and extends the strengths of several common machine learning algorithms, such as algorithms in self-organizing neural networks, ensemble methods and deep neural networks. The increased expression power is combined with the explanation power of self-organizing networks. Our algorithm outperforms both deep neural networks and ensembles of deep neural networks. For our evaluation case, we use production monitoring data from a complex steel manufacturing process, where data is both high-dimensional and has many nonlinear interdependencies. In addition to the improved prediction score, the algorithm offers a new deep-learning based approach for how computational resources can be focused in data exploration, since the algorithm points out areas of the input space that are more challenging to learn.
CITATION STYLE
Ståhl, N., Falkman, G., Mathiason, G., & Karlsson, A. (2018). A self-organizing ensemble of deep neural networks for the classification of data from complex processes. In Communications in Computer and Information Science (Vol. 855, pp. 248–259). Springer Verlag. https://doi.org/10.1007/978-3-319-91479-4_21
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