Design of the 2015 ChaLearn AutoML challenge

Guyon, Isabelle, Cawley, Gavin, Bennett, Kristin, Jair Escalente, Hugo, Escalera, Sergio, Ho, Tin Kam, Macia, Nuria, Ray, Bisakha, Saeed, Mehreen, Statnikov, Alexander and Viegas, Evelyne (2015) Design of the 2015 ChaLearn AutoML challenge. In: Proceedings of International Joint Conference on Neural Networks (IJCNN). IEEE Press.

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Abstract

ChaLearn is organizing the Automatic Machine Learning (AutoML) contest for IJCNN 2015, which challenges participants to solve classification and regression problems without any human intervention. Participants' code is automatically run on the contest servers to train and test learning machines. However, there is no obligation to submit code; half of the prizes can be won by submitting prediction results only. Datasets of progressively increasing difficulty are introduced throughout the six rounds of the challenge. (Participants can enter the competition in any round.) The rounds alternate phases in which learners are tested on datasets participants have not seen, and phases in which participants have limited time to tweak their algorithms on those datasets to improve performance. This challenge will push the state of the art in fully automatic machine learning on a wide range of real-world problems. The platform will remain available beyond the termination of the challenge.

Item Type: Book Section
Faculty \ School: Faculty of Science > School of Computing Sciences

University of East Anglia > Faculty of Science > Research Groups > Computational Biology (subgroups are shown below) > Machine learning in computational biology
Depositing User: Pure Connector
Date Deposited: 02 Feb 2016 13:13
Last Modified: 22 Apr 2020 10:54
URI: https://ueaeprints.uea.ac.uk/id/eprint/56932
DOI: 10.1109/IJCNN.2015.7280767

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