# Representation Learning of Tangled Key-Value Sequence Data for Early Classification

arXiv: https://arxiv.org/abs/2404.07454 · HTML: https://mlsanity.com/paper/2404.07454/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 50 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Approaches and Applications of Early Classification of Time Series: A Review](https://mlsanity.com/paper/2005.02595/) (2020)
- [k-fingerprinting: a Robust Scalable Website Fingerprinting Technique](https://mlsanity.com/paper/1509.00789/) (2015)
- [SDM: Sequential Deep Matching Model for Online Large-scale Recommender System](https://mlsanity.com/paper/1909.00385/) (2019)
- [Deep Learning for Encrypted Traffic Classification: An Overview](https://mlsanity.com/paper/1810.07906/) (2018)
- [TEASER: Early and Accurate Time Series Classification](https://mlsanity.com/paper/1908.03405/) (2019)
- [End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping](https://mlsanity.com/paper/1901.10681/) (2019)
- [Multitask Learning for Network Traffic Classification](https://mlsanity.com/paper/1906.05248/) (2019)
- [Stop&Hop: Early Classification of Irregular Time Series](https://mlsanity.com/paper/2208.09795/) (2022)
- [Snippet Policy Network for Multi-class Varied-length ECG Early Classification](https://mlsanity.com/paper/2107.13361/) (2021)
- [Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling](https://mlsanity.com/paper/2106.07356/) (2021)
- [Session-aware Linear Item-Item Models for Session-based Recommendation](https://mlsanity.com/paper/2103.16104/) (2021)

## Abstract (arXiv)

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%.