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In real-world classification tasks, deep neural networks show innovative performance in various fields. However, traditional classification methods are constructed based on a set of predefined classes and force unknown classes that determine their categorization into one of the predefined classes. This problem is addressed by the research field known as open-set recognition. Existing open-set recognition methods claim that the unique features of unknowns cannot be maintained by using only the final features. In other words, the various feature extraction methods should be considered to effectively reflect the characteristics of unknowns. In this study, we propose an open-set recognition model equipped with multi-feature extraction for multivariate time series data. The results of experiments with various multivariate time series datasets indicate that the proposed method shows improved capability to detect unknown classes while maintaining good predictive performance.

Multivariate Time Series Open-Set Recognition Using Multi-Feature Extraction and Reconstruction