Representation Learning of Tangled Key-Value Sequence Data for Early Classification
<|reference_start|>Representation Learning of Tangled Key-Value Sequence Data for Early Classification: Key-value sequence data has become ubiquitous and naturally appears in a variety of real-world applications, ranging from the user-product purchasing sequences in e-commerce, to network packet sequences forwarded by routers in networking. Classifying these key-value sequences is important in many scenarios such as user profiling and malicious applications identification. In many time-sensitive scenarios, besides the requirement of classifying a key-value sequence accurately, it is also desired to classify a key-value sequence early, in order to respond fast. However, these two goals are conflicting in nature, and it is challenging to achieve them simultaneously. In this work, we formulate a novel tangled key-value sequence early classification problem, where a tangled key-value sequence is a mixture of several concurrent key-value sequences with different keys. The goal is to classify each individual key-value sequence sharing a same key both accurately and early. To address this problem, we propose a novel method, i.e., Key-Value sequence Early Co-classification (KVEC), which leverages both inner- and inter-correlations of items in a tangled key-value sequence through key correlation and value correlation to learn a better sequence representation. Meanwhile, a time-aware halting policy decides when to stop the ongoing key-value sequence and classify it based on current sequence representation. Experiments on both real-world and synthetic datasets demonstrate that our method outperforms the state-of-the-art baselines significantly. KVEC improves the prediction accuracy by up to $4.7 - 17.5\%$ under the same prediction earliness condition, and improves the harmonic mean of accuracy and earliness by up to $3.7 - 14.0\%$.<|reference_end|>
@article{duan2024representation,
    title={Representation Learning of Tangled Key-Value Sequence Data for Early
  Classification},
    author={Tao Duan, Junzhou Zhao, Shuo Zhang, Jing Tao, Pinghui Wang},
    journal={arXiv preprint arXiv:2404.07454},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07454},
    primaryClass={cs.LG cs.NI}
}