🤖 AI Summary
This work addresses the challenges of multiclass classification—such as high inter-class similarity, class imbalance, and significant distributional discrepancies—where a single model often struggles to simultaneously capture complex functional relationships and sharp decision boundaries. The authors propose LFS-FRAME, a novel framework featuring a leakage-free stacking mechanism that synergistically combines the global function approximation capability of Kolmogorov–Arnold Networks (KANs) with the local rule-learning strength of XGBoost. By employing rigorous out-of-fold validation to generate unbiased meta-features, the framework effectively integrates functional learning with probabilistic meta-learning. Evaluated across multiple datasets, the method achieves 89.85% accuracy in main-group identification and 81.74% in sub-group recognition, substantially outperforming strong single-model baselines and enhancing both robustness and generalization in multiclass classification tasks.
📝 Abstract
Multiclass classification is a fundamental problem across a wide range of domains. It is still challenging due to possession of high inter-class similarity, class imbalance datasets, and variability in data distributions. Rule-based classifiers such as XGBoost often achieve stronger performance on structured features, but they are limited in capturing smooth functional relationships among variables. Similarly, neural network models can represent complex nonlinear interactions but frequently suffer from overfitting and generalization issues. To address these limitations, we propose LFS-FRAME, a Leakage-Free Stacked ensemble framework that integrates functional learning using Kolmogorov-Arnold Networks (KAN) and rule-based learning via XGBoost for robust multiclass classification.
The proposed framework constructs unbiased meta-features by employing a strict out-of-fold stacking strategy to ensure complete isolation between training and validation data hence preventing performance leakage. By learning over probabilistic outputs from heterogeneous base learners, the meta-classifier effectively exploits both global functional patterns and sharp decision boundaries present in the complex data. Experimental evaluations on multi-class datasets demonstrate that LFS-FRAME improves performance metrics, and overall accuracy is 89.85% in identifying major families and 81.74% in identifying sub-families relative to strong single-model baselines. These results highlight the effectiveness of leakage-free functional and rule-based stacking for reliable and generalizable multiclass classification.