Multi-Target Regression Based on Multi-Layer Sparse Structure and Its Application in Warships Scheduled Maintenance Cost Prediction
نویسندگان
چکیده
The scheduled maintenance cost of warships is the essential prerequisite and economic foundation to guarantee effective implementation maintenance, which directly influences quality efficiency operations. This paper proposes a multi-target regression algorithm based on multi-layer sparse structure (MTR-MLS) algorithm, achieve simultaneous prediction subentry costs warship total estimated by summing predicted values different costs. In MTR-MLS, kernel technique employed map inputs higher dimensional space for decoupling complex input–output nonlinear relationships. By deploying matrix, MTR-MLS achieves latent variable model can explicitly encode inter-target correlations via l2,1-norm-based learning. Meanwhile, noises are encoded diminish influence while exploiting among targets. An alternating optimization proposed solve objective function. Extensive experimental evaluation real-world datasets show that method consistently outperforms state-of-the-art algorithms, demonstrates its great effectiveness maintenance.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2022
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app13010435