To address the challenge of multimodal anomaly data governance in ship maintenance-cost prediction, this study proposes a three-stage hybrid data-cleansing framework integrating physical constraints and intelligent optimization. First, we construct a multi-dimensional engineering physical constraints rule base to identify contradiction-type anomalies through ship hydrodynamics validation and business logic verification. Second, we develop a Feature-Weighted Isolation Forest Algorithm (W-iForest) algorithm that dynamically optimizes feature selection strategies by incorporating rule triggering frequency and expert knowledge, thereby enhancing detection efficiency for discrete-type anomalies. Finally, we create a Genetic Algorithm-Ant Colony Optimization Collaborative Random Forest (GA-ACO-RF) to resolve local optima issues in high-dimensional missing data imputation. Experimental results demonstrate that the proposed method achieves a physical compliance rate of 88.2% on ship-maintenance datasets, with a 25% reduction in RMSE compared to conventional prediction methods, validating its superior data governance capability and prediction accuracy under complex operating conditions. This research establishes a reliable data preprocessing paradigm for maritime operational assurance, exhibiting substantial engineering applicability in real-world maintenance scenarios.

Software effort estimation (SEE) serves as a cornerstone of effective software project management, and case-based reasoning (CBR) stands out as one of the most extensively adopted approaches within this domain. Nevertheless, CBR-based SEE models are still plagued by two critical challenges: conventional case retrieval mechanisms lack the ability to differentiate the relative importance of various features, and data scarcity remains a persistent bottleneck. Both issues significantly compromise the estimation accuracy and interpretability of the models. To address these limitations, we propose a SHAP–Mixup synergistic framework that enhances both feature-aware similarity learning and data distribution modeling. Specifically, we introduce (1) a stability-aware SHAP-weighted similarity metric that integrates both the magnitude and variance of feature contributions to improve retrieval robustness, and (2) a density-aware Mixup augmentation strategy that generates synthetic samples guided by local data manifold structure rather than random interpolation. Experimental results on seven benchmark datasets demonstrate that the proposed method reduces MAE and MSE by up to 20.2% on average compared to baseline CBR models, while consistently improving Pred(0.25). Furthermore, by enhancing model interpretability, the proposed method equips project managers with actionable insights into the key drivers of software effort, thereby facilitating more informed and efficient resource allocation. Building on these findings, this study provides a novel and effective pathway for developing SEE models that are more accurate, robust, and transparent.

This paper proposes a novel instance selection (IS) method for multi-target regression (MTR). The proposed method introduces Sparse Modeling Representative Selection (SMRS) to characterize the self-representativeness of instances, where a block-sparse coefficient matrix is learned to identify representative training samples. Considering that MTR data contain both input-space structures and multi-target output-space dependencies, we assume that the self-representative relationships in the input and output spaces should be consistent. Based on this assumption, a Frobenius-norm-based discrepancy minimization term and a Hilbert-Schmidt Independence Criterion (HSIC)-based relevance maximization term are incorporated to encourage the model to learn consistent and statistically dependent self-representation structures across the two spaces. To capture nonlinear instance relationships, the proposed model is further extended to a kernelized formulation. In addition, an ensemble framework is developed to exploit target correlations from multiple target-aware subspaces, thereby improving the robustness of instance selection. A parameter-free adaptive thresholding strategy is also designed to automatically determine the selected subset from the learned instance-score distribution. Extensive experiments on multiple benchmark MTR datasets demonstrate that the proposed methods can effectively reduce the training data size while preserving or improving downstream predictive performance compared with representative instance selection and data reduction baselines.