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AutoDAL: Distributed Active Learning with Automatic Hyperparameter Selection

Publication ,  Journal Article
Chen, X; Wujek, B
Published in: Proceedings of the AAAI Conference on Artificial Intelligence
April 3, 2020

Automated machine learning (AutoML) strives to establish an appropriate machine learning model for any dataset automatically with minimal human intervention. Although extensive research has been conducted on AutoML, most of it has focused on supervised learning. Research of automated semi-supervised learning and active learning algorithms is still limited. Implementation becomes more challenging when the algorithm is designed for a distributed computing environment. With this as motivation, we propose a novel automated learning system for distributed active learning (AutoDAL) to address these challenges. First, automated graph-based semi-supervised learning is conducted by aggregating the proposed cost functions from different compute nodes in a distributed manner. Subsequently, automated active learning is addressed by jointly optimizing hyperparameters in both the classification and query selection stages leveraging the graph loss minimization and entropy regularization. Moreover, we propose an efficient distributed active learning algorithm which is scalable for big data by first partitioning the unlabeled data and replicating the labeled data to different worker nodes in the classification stage, and then aggregating the data in the controller in the query selection stage. The proposed AutoDAL algorithm is applied to multiple benchmark datasets and a real-world electrocardiogram (ECG) dataset for classification. We demonstrate that the proposed AutoDAL algorithm is capable of achieving significantly better performance compared to several state-of-the-art AutoML approaches and active learning algorithms.

Duke Scholars

Published In

Proceedings of the AAAI Conference on Artificial Intelligence

DOI

EISSN

2374-3468

ISSN

2159-5399

Publication Date

April 3, 2020

Volume

34

Issue

04

Start / End Page

3537 / 3544

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Chen, X., & Wujek, B. (2020). AutoDAL: Distributed Active Learning with Automatic Hyperparameter Selection. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 3537–3544. https://doi.org/10.1609/aaai.v34i04.5759
Chen, Xu, and Brett Wujek. “AutoDAL: Distributed Active Learning with Automatic Hyperparameter Selection.” Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3537–44. https://doi.org/10.1609/aaai.v34i04.5759.
Chen X, Wujek B. AutoDAL: Distributed Active Learning with Automatic Hyperparameter Selection. Proceedings of the AAAI Conference on Artificial Intelligence. 2020 Apr 3;34(04):3537–44.
Chen, Xu, and Brett Wujek. “AutoDAL: Distributed Active Learning with Automatic Hyperparameter Selection.” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, Association for the Advancement of Artificial Intelligence (AAAI), Apr. 2020, pp. 3537–44. Crossref, doi:10.1609/aaai.v34i04.5759.
Chen X, Wujek B. AutoDAL: Distributed Active Learning with Automatic Hyperparameter Selection. Proceedings of the AAAI Conference on Artificial Intelligence. Association for the Advancement of Artificial Intelligence (AAAI); 2020 Apr 3;34(04):3537–3544.

Published In

Proceedings of the AAAI Conference on Artificial Intelligence

DOI

EISSN

2374-3468

ISSN

2159-5399

Publication Date

April 3, 2020

Volume

34

Issue

04

Start / End Page

3537 / 3544

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)