# Open challenges for Machine Learning based Early Decision-Making research

arXiv: https://arxiv.org/abs/2204.13111 · HTML: https://mlsanity.com/paper/2204.13111/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 109 references that are in the map.
- First posted on arXiv 27 Apr 2022.

## Builds on (most cited first, 24 of 24)

- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [Multiple Instance Learning: A Survey of Problem Characteristics and Applications](https://mlsanity.com/paper/1612.03365/) (2016)
- [Precision and Recall for Time Series](https://mlsanity.com/paper/1803.03639/) (2018)
- [Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car](https://mlsanity.com/paper/1704.07911/) (2017)
- [Approaches and Applications of Early Classification of Time Series: A Review](https://mlsanity.com/paper/2005.02595/) (2020)
- [Fake News Early Detection: An Interdisciplinary Study](https://mlsanity.com/paper/1904.11679/) (2019)
- [Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks](https://mlsanity.com/paper/2004.04834/) (2020)
- [Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring](https://mlsanity.com/paper/1905.09568/) (2019)
- [Alarm-Based Prescriptive Process Monitoring](https://mlsanity.com/paper/1803.08706/) (2018)
- [From Weakly Supervised Learning to Biquality Learning: an Introduction](https://mlsanity.com/paper/2012.09632/) (2020)
- [Classifiers With a Reject Option for Early Time-Series Classification](https://mlsanity.com/paper/1312.3989/) (2013)
- [Computer vision in automated parking systems: Design, implementation and challenges](https://mlsanity.com/paper/2104.12537/) (2021)
- [Early Classification for Agricultural Monitoring from Satellite Time Series](https://mlsanity.com/paper/1908.10283/) (2019)
- [Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape](https://mlsanity.com/paper/2012.11354/) (2020)
- [Unsupervised anomaly detection for a Smart Autonomous Robotic Assistant Surgeon (SARAS)using a deep residual autoencoder](https://mlsanity.com/paper/2104.11008/) (2021)
- [Graphs in machine learning: an introduction](https://mlsanity.com/paper/1506.06962/) (2015)
- [Early Classification of Time Series. Cost-based Optimization Criterion and Algorithms](https://mlsanity.com/paper/2005.09945/) (2020)
- [Importance Reweighting for Biquality Learning](https://mlsanity.com/paper/2010.09621/) (2020)
- [Friend or Foe? Fake Profile Identification in Online Social Networks](https://mlsanity.com/paper/1303.3751/) (2013)
- [Early and Revocable Time Series Classification](https://mlsanity.com/paper/2109.10285/) (2021)
- [Early Classification of Time Series is Meaningful](https://mlsanity.com/paper/2104.13257/) (2021)

## Built on by (most cited first, 6 of 6)

- [Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoring](https://mlsanity.com/paper/2307.05939/) (2023)
- [Early Classification of Time Series: A Survey and Benchmark](https://mlsanity.com/paper/2406.18332/) (2024)
- [Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluation](https://mlsanity.com/paper/2501.07999/) (2025)
- [Alert: Learning Trigger Functions for Early Classification of Time Series using Deep-RL](https://mlsanity.com/paper/2502.06584/) (2025)
- [TURBOTEST: Learning When Less is Enough through Early Termination of Internet Speed Tests](https://mlsanity.com/paper/2510.21141/) (2025)
- [Early Classification of Time Series in Non-Stationary Cost Regimes](https://mlsanity.com/paper/2602.00918/) (2026)

## Abstract (arXiv)

More and more applications require early decisions, i.e. taken as soon as possible from partially observed data. However, the later a decision is made, the more its accuracy tends to improve, since the description of the problem to hand is enriched over time. Such a compromise between the earliness and the accuracy of decisions has been particularly studied in the field of Early Time Series Classification. This paper introduces a more general problem, called Machine Learning based Early Decision Making (ML-EDM), which consists in optimizing the decision times of models in a wide range of settings where data is collected over time. After defining the ML-EDM problem, ten challenges are identified and proposed to the scientific community to further research in this area. These challenges open important application perspectives, discussed in this paper.