MNIST数据库
人工智能
计算机科学
修剪
机器学习
核(代数)
遗传算法
深度学习
过程(计算)
可靠性(半导体)
算法
数学
功率(物理)
物理
组合数学
量子力学
农学
生物
操作系统
作者
Wangbo Shen,Weiwei Lin,Yulei Wu,Fang Shi,Wentai Wu,Keqin Li
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-02-01
卷期号:19 (2): 1569-1580
被引量:2
标识
DOI:10.1109/tii.2022.3206817
摘要
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.
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