AutoDAL: Distributed Active Learning with Automatic Hyperparameter Selection

Author:

Chen Xu,Wujek Brett

Abstract

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.

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Subject

General Medicine

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Automated machine learning: past, present and future;Artificial Intelligence Review;2024-04-18

2. Knowledge-Aware Federated Active Learning with Non-IID Data;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

3. A systematic literature review on AutoML for multi-target learning tasks;Artificial Intelligence Review;2023-08-10

4. Active Learning With Co-Auxiliary Learning and Multi-Level Diversity for Image Classification;IEEE Transactions on Circuits and Systems for Video Technology;2023-08

5. A Survey on Active Learning: State-of-the-Art, Practical Challenges and Research Directions;Mathematics;2023-02-06

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