EvoTSC evolves lightweight feature learning models for time series classification via genetic programming with embedded expert knowledge and Pareto tournament selection, outperforming eleven benchmarks on univariate datasets.
Highly comparative feature-based time-series classification
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ERIS combines energy-guided calibration, weight-level orthogonality, and auxiliary adversarial generalization to produce shift-robust representations for out-of-distribution time series classification.
citing papers explorer
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EvoTSC: Evolving Feature Learning Models for Time Series Classification via Genetic Programming
EvoTSC evolves lightweight feature learning models for time series classification via genetic programming with embedded expert knowledge and Pareto tournament selection, outperforming eleven benchmarks on univariate datasets.
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ERIS: An Energy-Guided Feature Disentanglement Framework for Out-of-Distribution Time Series Classification
ERIS combines energy-guided calibration, weight-level orthogonality, and auxiliary adversarial generalization to produce shift-robust representations for out-of-distribution time series classification.