@inproceedings{731303494fee4dab91b034692e9af79c,
title = "AutoGRD: Model recommendation through graphical dataset representation",
abstract = "The widespread use of machine learning algorithms and the high level of expertise required to utilize them have fuelled the demand for solutions that can be used by non-experts. One of the main challenges non-experts face in applying machine learning to new problems is algorithm selection - the identification of the algorithm(s) that will deliver top performance for a given dataset, task, and evaluation measure. We present AutoGRD, a novel meta-learning approach for algorithm recommendation. AutoGRD first represents datasets as graphs and then extracts their latent representation that is used to train a ranking meta-model capable of accurately recommending top-performing algorithms for previously unseen datasets. We evaluate our approach on 250 datasets and demonstrate its effectiveness both for classification and regression tasks. AutoGRD outperforms state-of-the-art meta-learning and Bayesian methods.",
keywords = "Algorithm selection, AutoML, Classification, Dataset representation, Graph embedding, Meta-learning, Regression",
author = "Noy Cohen-Shapira and Lior Rokach and Bracha Shapira and Gilad Katz and Roman Vainshtein",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 28th ACM International Conference on Information and Knowledge Management, CIKM 2019 ; Conference date: 03-11-2019 Through 07-11-2019",
year = "2019",
month = nov,
day = "3",
doi = "10.1145/3357384.3357896",
language = "English",
series = "International Conference on Information and Knowledge Management, Proceedings",
publisher = "Association for Computing Machinery",
pages = "821--830",
booktitle = "CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management",
}