Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Open challenges for Machine Learning based Early Decision-Making research

Open challenges for Machine Learning based Early Decision-Making research 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGKDD Explorations Newsletter Association for Computing Machinery

Open challenges for Machine Learning based Early Decision-Making research

Download PDF
20 pages

Loading next page...
 
/lp/association-for-computing-machinery/open-challenges-for-machine-learning-based-early-decision-making-njYiTWmjxm
Publisher
Association for Computing Machinery
Copyright
Copyright © 2022 Copyright is held by the owner/author(s)
ISSN
1931-0145
eISSN
1931-0153
DOI
10.1145/3575637.3575643
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

ACM SIGKDD Explorations Newsletter – Association for Computing Machinery

Published: Dec 5, 2022

References

  • A review of machining monitoring systems based on artificial intelligence process models
    Abellan-Nebot, J. V.; Subir´on, F. R.
  • Early classification of time series
    Achenchabe, Y.; Bondu, A.; Cornu´ejols, A.; Dachraoui, A.
  • Early and revocable time series classification
    Achenchabe, Y.; Bondu, A.; Cornu´ejols, A.; Lemaire, V.
  • Ecots: Early classification in open time series
    Achenchabe, Y.; Bondu, A.; Cornu´ejols, A.; Lemaire, V.
  • Sentiment aware fake news detection on online social networks
    Ajao, O.; Bhowmik, D.; Zargari, S.
  • Street-view change detection with deconvolutional networks
    Alcantarilla, P. F.; Stent, S.; Ros, G.; Arroyo, R.; Gherardi, R.
  • Machine learning-based delayaware uav detection over encrypted wi-fi traffic
    Alipour-Fanid, A.; Dabaghchian, M.; Wang, N.; Wang, P.; Zhao, L.; Zeng, K.
  • Detection of fake twitter accounts with machine learning algorithms
    Aydin, I.; Mehmet, S.; Salur, M. U.
  • A note on sequential detection with exponential penalty for the delay
    Beibel, M.
  • Machine learning for data streams: with practical examples in MOA
    Bifet, A.; Gavald'a, R.; Holmes, G.; Pfahringer, B.
  • End to end learning for self-driving cars
    Bojarski, M.; Del Testa, D.; Dworakowski, D.; Firner, B.; Flepp, B.; Goyal, P.; Jackel, L. D.; Monfort, M.; Muller, U.; Zhang, J.
  • Explaining how a deep neural network trained with end-to-end learning steers a car
    Bojarski, M.; Yeres, P.; Choromanska, A.; Choromanski, K.; Firner, B.; Jackel, L.; Muller, U.
  • Friend or faux: Graph-based early detection of fake accounts on social networks
    Breuer, A.; Eilat, R.; Weinsberg, U.
  • A systematic literature review of machine learning methods applied to predictive maintenance
    Carvalho, T. P.; Soares, F. A. A. M. N.; Vita, R.; Francisco, R. da P.; Basto, J. P.; Alcal´a, S. G. S.
  • Suicide risk assessment using machine learning and social networks: A scoping review
    Castillo-S´anchez, G.; Marques, G.; Dorronzoro, E.; Rivera-Romero, O.; Franco-Mart´n, M.; De la Torre-D´ez, I.
  • Time-series forecasting
    Chatfield, C.
  • Early classification of individual electricity consumptions
    Dachraoui, A.; Bondu, A.; Cornuejols, A.
  • Early classification of time series as a non myopic sequential decision making problem
    Dachraoui, A.; Bondu, A.; Cornu´ejols, A.
  • Relational data mining
    Dzeroski, S.
  • The foundations of cost-sensitive learning
    Elkan, C.
  • Social networks fake profiles detection using machine learning algorithms
    Elyusufi, Y.; Elyusufi, Z.
  • Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations
    Fagnant, D. J.; Kockelman, K.