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Erschienen in:
2018 | OriginalPaper | Buchkapitel
A Self-Organizing Ensemble of Deep Neural Networks for the Classification of Data from Complex Processes
verfasst von : Niclas Ståhl, Göran Falkman, Gunnar Mathiason, Alexander Karlsson
Erschienen in: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications
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Abstract
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.