Auto-CASH: A meta-learning embedding approach for autonomous classification algorithm selection

Mu, Tianyu; Wang, Hongzhi; Wang, Chunnan; Liang, Zheng; Shao, Xinyue

doi:10.1016/j.ins.2022.01.040

Cited by 13 publications

(2 citation statements)

References 18 publications

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“…In contrast to conventional machine learning, meta-learning [31] considers improving the problem-solving ability of meta-learning models by training some tasks such that they can provide suitable solutions to problems that have not been solved previously. Meta-learning is performed by the cooperation of the base and the meta-learner.…”

Section: Improved Meta-learning Algorithm a Meta-learningmentioning

confidence: 99%

A Novel Method for Few-Shot Specific Emitter Identification in Non-Cooperative Scenarios

Xie¹,

Zhang²,

Zhong

2023

IEEE Access

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Obtaining larger category-label-containing training signal datasets in non-cooperative scenarios is difficult. Moreover, employing smaller labeled signal datasets for specific emitter identification is technically challenging. Therefore, we propose a novel method for few-shot SEI. We first design a bispectral analysis and Radon transformation-based signal preprocessing scheme to obtain feature vectors that effectively characterize the radio frequency fingerprints. The feature vectors are then fed to a network model for feature learning. Moreover, a meta-learning algorithm is applied to the network model to adapt to few-shot feature learning. The conventional meta-learning algorithm is improved to develop a novel algorithm involving latent embedding optimization for meta-learning. The proposed method extracts lowdimensional key features from high-dimensional input data and evaluates the distance and degree of feature dispersion. The resulting information is employed in sample point prediction. The algorithm effectively achieves few-shot SEI and offers stable and efficient recognition after training with a minimum of forty samples. This method identifies emitter individuals under multiple modulation types and exhibits scalability in identifying the emitter numbers. Moreover, it offers adaptability in identifying the emitter individuals under multiple propagation channel types.

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Section: Improved Meta-learning Algorithm a Meta-learningmentioning

confidence: 99%

A Novel Method for Few-Shot Specific Emitter Identification in Non-Cooperative Scenarios

Xie¹,

Zhang²,

Zhong

2023

IEEE Access

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“…Accordingly, the meta-learner can provide more optimal initial values to the temporary network on the new tasks, thus making the temporary network achieve training fitting through a few iterations on the condition of few samples. The training results are fed back to the meta-learner to improve its generalization performance further [30]. Therefore, the meta-learning algorithm is considered suitable for few-shot training.…”

Section: Meta-learningmentioning

confidence: 99%

Few-Shot Specific Emitter Identification Based on Variational Mode Decomposition and Meta-Learning

Xie¹,

Zhang²,

Zhong

2022

Wireless Communications and Mobile Computing

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Recently, deep learning has become the mainstream solution to solve specific emitter identification (SEI) problems. However, because large amounts of labeled signal samples cannot be obtained in noncooperative scenarios, the performance of deep learning-based data-driven methods for SEI was limited. As a result, a novel SEI method targeted on few-shot was proposed in this study. First, the received signal was preprocessed based on variational mode decomposition and the Hilbert analysis to obtain the Hilbert time-frequency spectrum. Subsequently, a classification neural network model was built and trained with a small number of Hilbert time-frequency spectrum samples through meta-learning. This model could identify specific emitters with limited training samples. The experimental results showed that this method accomplishes network training with as few as 80 training samples while obtaining a good level of generalization and effectively identifying different emitter individuals. In addition, this method exhibits a strong robustness to noise by maintaining an identification accuracy of more than 80% in channels with low signal-to-noise ratios. Finally, the proposed method demonstrated better identification performances than other existing methods with its capability to effectively solve SEI problems in the few-shot scenario.

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A data-driven meta-learning recommendation model for multi-mode resource constrained project scheduling problem

Chu

Gao

et al. 2023

Computers & Operations Research

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Auto-CASH: A meta-learning embedding approach for autonomous classification algorithm selection

Cited by 13 publications

References 18 publications

A Novel Method for Few-Shot Specific Emitter Identification in Non-Cooperative Scenarios

A Novel Method for Few-Shot Specific Emitter Identification in Non-Cooperative Scenarios

Few-Shot Specific Emitter Identification Based on Variational Mode Decomposition and Meta-Learning

A data-driven meta-learning recommendation model for multi-mode resource constrained project scheduling problem

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