Asma DachraouiDassault Systèmes · R&D
Asma Dachraoui
PhD Student
About
9
Publications
2,361
Reads
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98
Citations
Citations since 2017
Introduction
Actually PhD student at EDF Research & Development and AgroParistech, my PhD thesis deals with the problem of early classification of time series.
Additional affiliations
January 2012 - November 2016
Education
December 2012 - December 2015
September 2011 - September 2012
Publications
Publications (9)
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![Economy-γ\documentclass[12pt]{minimal} \usepackage{amsmath}...](publication/352076049/figure/fig2/AS:1162346261168136@1654136352531/Economy-gdocumentclass12ptminimal-usepackageamsmath-usepackagewasysym_Q320.jpg)
An increasing number of applications require to recognize the class of an incoming time series as quickly as possible without unduly compromising the accuracy of the prediction. In this paper, we put forward a new optimization criterion which takes into account both the cost of misclassification and the cost of delaying the decision. Based on this...
An increasing number of applications require to recognize the class of an incoming time series as quickly as possible without unduly compromising the accuracy of the prediction. In this paper, we put forward a new optimization criterion which takes into account both the cost of misclassification and the cost of delaying the decision. Based on this...
Early classification of time series is becoming increasingly a valuable task for assisting in decision making
process in many application domains. In this setting, information can be gained by waiting for more evidences to arrive, thus helping to make better decisions that incur lower misclassification costs, but, meanwhile, the cost associated wi...
Aiding to make decisions as early as possible by learning from past experiences is becoming increasingly important in many application domains. In these settings , information can be gained by waiting for more evidences to arrive, thus helping to make better decisions that incur lower misclassification costs, but, meanwhile, the cost associated wit...
Classification of time series as early as possible is a valuable goal. Indeed, in many application domains, the earliest the decision, the more rewarding it can be. Yet, often, gathering more information allows one to get a better decision. The optimization of this time vs. accuracy tradeoff must generally be solved online and is a complex problem....
The incoming smart grid represents a significant break for the European utilities in terms of data volume to be processed. In France, one year of individual consumptions represents more than 600 billion data points. Since real data is not yet available, our objective consists in simulating realistic individual consumptions. A new generative model o...
Early classification approaches deal with the problem of re-liably labeling incomplete time series as soon as possible given a level of confidence. While developing new approaches for this problem has been getting increasing attention recently, their evaluation are still not thor-oughly considered. In this article, we propose a new evaluation proto...
Early classification approaches deal with the problem of reliably labeling incomplete time series as soon as possible given a level of confidence. While developing new approaches for this problem has been getting increasing attention recently, their evaluation are still not thoroughly considered. In this article, we propose a new evaluation protoco...
EDF hires special contracts with costumers to flatten the consumption peaks. Smart meters are able to record consumptions and will be set up over 35 millions households. In this paper, we highlight the interest of early classification for detecting the households which probably contribute to the evening peak. The proposed approach is based on a col...
Projects
Project (1)
Reddit: https://www.reddit.com/r/EarlyMachineLearning/
GitHub: https://github.com/ML-EDM/ML-EDM/
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 project 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.
