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Evolving Deep Multiple Kernel Learning Networks Through Genetic Algorithms | IEEE Journals & Magazine | IEEE Xplore

Evolving Deep Multiple Kernel Learning Networks Through Genetic Algorithms


Abstract:

Today's Industrial Internet of Things (IIoT) have achieved excellent manufacturing efficiency and automation results by leveraging machine learning (ML) and deep learning...Show More

Abstract:

Today's Industrial Internet of Things (IIoT) have achieved excellent manufacturing efficiency and automation results by leveraging machine learning (ML) and deep learning (DL). However, trustworthiness of ML/DL brings significant challenges to IIoT. This article proposes an evolving deep multiple kernel learning network through genetic algorithm (KNGA). Our KNGA method uses genetic algorithm (GA) to find the best deep multiple kernel learning structure, including the weights and the topology of the model. Compared with the current well-known models, KNGA has advantages in three aspects: 1) It can achieve good results without using many samples during model training; 2) the model can evolve in the process of training, including self-growth, and self-pruning; and 3) its trustworthiness and reliability can be guaranteed. Moreover, the whole model ensures excellent performance and requires manual adjustment of only a few parameters. Extensive experiments on the UCI, KEEL, Caltech256, and MNIST datasets demonstrate the effectiveness and trustworthiness of the proposed method.
Published in: IEEE Transactions on Industrial Informatics ( Volume: 19, Issue: 2, February 2023)
Page(s): 1569 - 1580
Date of Publication: 15 September 2022

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