adaptive decision fusion

Fuzzy rule models offer strong interpretability but suffer from poor scalability and susceptibility to overfitting in complex tasks and large-scale data scenarios. To address these limitations, this paper proposes a novel ensemble framework integrating gradient boosting with fuzzy rule-based base learners. We introduce a dynamic control factor that adaptively adjusts the weights of fuzzy base models in each boosting iteration, simultaneously serving as a regularizer and performance optimizer. Additionally, we design a validation-set-driven, sample-level correction mechanism to enhance generalization and ensemble diversity. Experimental results demonstrate that our approach significantly mitigates overfitting, reduces rule complexity (e.g., fewer rules and shorter antecedents), and preserves high model interpretability and maintainability. The method thus provides a practical pathway for deploying interpretable AI in complex industrial applications.

Existing ensemble methods typically rely on a single evaluation criterion for weight assignment, failing to fully exploit multidimensional prior performance information of base classifiers—thus limiting overall model performance. This paper proposes a cooperative-game-theoretic multi-criteria weighted ensemble learning framework. It is the first to introduce the Shapley value into multi-criteria ensemble learning, quantifying each classifier’s marginal contribution across multiple dimensions—including accuracy, stability, and class-wise sensitivity—and integrating these via multi-criteria decision analysis for dynamic weight allocation. The approach ensures both comprehensiveness and fairness in decision-making. Evaluated on the OpenML-CC18 benchmark, it significantly outperforms mainstream weighted ensemble methods, achieving improvements in classification accuracy, robustness, and generalization ability, while effectively mitigating class imbalance and overfitting.

Integrating hypothesis testing results across heterogeneous multi-source studies—some reporting only binary significance decisions, others only FDR control levels—poses a fundamental challenge for rigorous, unified FDR control. Method: We propose the Integrated Ranking and Thresholding (IRT) framework, which operates solely on binary rejection decisions, a prespecified global FDR level, and the set of hypotheses—requiring neither raw data, p-values, nor effect sizes. IRT employs nonparametric evidence aggregation and a ranking-driven thresholding mechanism, circumventing traditional meta-analysis assumptions of statistical homogeneity and reliance on shared summary statistics. Contribution/Results: IRT is the first method to achieve theoretically guaranteed strong FDR control under non-shared statistical summaries. We prove its FDR control property rigorously; simulations demonstrate superior performance over state-of-the-art integration methods; and real-world application to multi-center genome-wide association studies confirms its practical utility and robustness.

This paper addresses insufficient risk diversification in portfolio optimization by proposing a structured ensemble learning framework based on multi-hypothesis prediction, unifying asset selection and weight optimization within a prediction-to-optimization pipeline subject to diversity constraints. Its key contributions include: (i) explicitly linking ensemble loss decomposition theory to portfolio diversification; (ii) introducing a pre-screening mechanism that dynamically balances predictive accuracy against structural diversity; and (iii) constructing a parameterized prediction set with controllable diversity and a supervised ensemble combiner (e.g., equal-weighted aggregation under squared loss). Empirical evaluation across over two decades of S&P 500 constituents and a global bond dataset comprising 1,300 instruments demonstrates significantly expanded achievable diversification bounds. The framework delivers robust, state-of-the-art performance in both single-period and multi-period portfolio allocation tasks.

This work addresses the lack of a unified Python framework for ensemble learning methods grounded in Composite Fusion Analysis (CFA), particularly in integrating Rank-Score Characteristic (RSC) functions with Cognitive Diversity (CD). To bridge this gap, we propose InFusionLayer—a general-purpose machine learning architecture inspired by CFA that, for the first time, unifies RSC and CD mechanisms within a single framework compatible with PyTorch, TensorFlow, and Scikit-learn. Requiring only a small set of base models, our approach achieves substantial performance gains in both unsupervised and supervised multi-class classification tasks. Extensive experiments across multiple computer vision benchmarks validate its efficacy, and the open-sourced implementation facilitates the practical adoption and broader dissemination of CFA within mainstream deep learning ecosystems.

This study addresses the financial losses and trust-related risks associated with credit default in credit card approval processes by proposing a novel multi-model ensemble mechanism based on Composite Fusion Analysis (CFA). The approach integrates five pre-trained machine learning models and enhances predictive performance through an optimized fusion strategy. Experimental results demonstrate that the proposed method achieves an accuracy of 89.13% on credit approval tasks, significantly outperforming both conventional machine learning techniques and existing ensemble methods. These findings underscore the effectiveness and innovation of the CFA framework in improving decision-making accuracy within credit risk assessment.

This work addresses the performance bottleneck in joint probabilistic forecasting of multivariate time series arising from the entangled modeling of marginal distributions and cross-series dependencies. To resolve this, the authors propose WIRED, a novel approach that decouples adaptive marginal prediction via expert aggregation from dependency structure reconstruction based on copulas. Specifically, marginal forecasts are generated using a CRPS-weighted adaptive ensemble, while dependencies are captured through Gaussian or Student-t copulas. The framework is rigorously evaluated via rolling-origin validation and ablation studies on both synthetic data and the EuStockMarkets benchmark. Results demonstrate the efficacy of the proposed architecture and reveal limitations in current CRPS-weighting strategies, showing that simple equal-weighting or bootstrap aggregation remains competitive for marginal modeling—offering new insights for designing regularized ensembles in probabilistic forecasting.

In small-sample experiments, noisy treatment effect estimates can lead to asymmetric losses in policy generalization and downstream operational decisions. To address this, this work proposes the PATRO method, which retains standard effect estimation while introducing data-agnostic dual adjustments—one for generalization decisions and another for downstream optimization—to minimize Bayes risk. This study is the first to decouple these two adjustments and systematically analyze their complementary or substitutive relationship, yielding a concise, transparent, and approximately Bayes-optimal decision framework. The adjustment parameters are solved via an alternating iterative algorithm that integrates Bayesian decision theory with a plug-in estimation framework. Both theoretical analysis and empirical results demonstrate that PATRO achieves performance close to or matching the Bayes optimum, significantly outperforming conventional approaches that directly plug point estimates into decision pipelines.

This work addresses the limited interpretability of existing machine learning methods in complex decision-making, which struggle to emulate human reasoning based on representative exemplars. To bridge this gap, the paper proposes a similarity-based hierarchical interpretable model that constructs predictions using an extremely small set of carefully selected pivotal instances. It uniquely integrates pivot selection with nearest-neighbor trees, oblique trees, and ensemble learning, establishing a data-modality-agnostic paradigm for highly interpretable modeling. Leveraging pretrained networks to uniformly process multimodal inputs—including tabular data, text, images, and time series—the method significantly outperforms current instance selection approaches across multiple benchmark datasets while achieving performance on par with state-of-the-art interpretable models.