TimeMIL: Advancing Multivariate Time Series Classification via a Time-aware Multiple Instance Learning
FOS: Computer and information sciences
Computer Science - Machine Learning
Machine Learning (cs.LG)
DOI:
10.48550/arxiv.2405.03140
Publication Date:
2024-05-05
AUTHORS (8)
ABSTRACT
Deep neural networks, including transformers and convolutional have significantly improved multivariate time series classification (MTSC). However, these methods often rely on supervised learning, which does not fully account for the sparsity locality of patterns in data (e.g., diseases-related anomalous points ECG). To address this challenge, we formally reformulate MTSC as a weakly problem, introducing novel multiple-instance learning (MIL) framework better localization interest modeling dependencies within series. Our approach, TimeMIL, formulates temporal correlation ordering time-aware MIL pooling, leveraging tokenized transformer with specialized learnable wavelet positional token. The proposed method surpassed 26 recent state-of-the-art methods, underscoring effectiveness TimeMIL MTSC.
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