Multi-Target Regression Based on Multi-Layer Sparse Structure and Its Application in Warships Scheduled Maintenance Cost Prediction

Abstract

The scheduled maintenance cost of warships is the essential prerequisite and economic foundation to guarantee the effective implementation of maintenance, which directly influences the quality and efficiency of maintenance operations. This paper proposes a multi-target regression algorithm based on multi-layer sparse structure (MTR-MLS) algorithm, to achieve simultaneous prediction of the subentry costs of warship scheduled maintenance, and the total cost of the maintenance is estimated by summing the predicted values of the different subentry costs. In MTR-MLS, the kernel technique is employed to map the inputs to the higher dimensional space for decoupling the complex input–output nonlinear relationships. By deploying the structure matrix, MTR-MLS achieves a latent variable model which can explicitly encode the inter-target correlations via l2,1-norm-based sparse learning. Meanwhile, the noises are encoded to diminish the influence of noises while exploiting the correlations among targets. An alternating optimization algorithm is proposed to solve the objective function. Extensive experimental evaluation on real-world datasets and datasets of warships scheduled maintenance cost show that the proposed method consistently outperforms the state-of-the-art algorithms, which demonstrates its great effectiveness for cost prediction of warships scheduled maintenance.

Keywords

• warships scheduled maintenance
• multi-target regression
• sparse learning
• cost prediction