Analytics

Standard ensemble classifiers assign weights based on performance over a single feature space, leaving multi-scale fractional representations unexplored as a tabular augmentation strategy. This study proposes the FRAE (Fractional-order Recalibrated Adaptive Ensemble) framework, which applies Grünwald–Letnikov (GL) derivatives at four memory orders as tabular feature augmentation and couples each view’s memory order to its ensemble weight via an α-specific power exponent. FRAE was evaluated on three open-access cancer datasets—WAW-TACE HCC (n = 198), Ye Prostate Cancer (C1 n = 298; C2 n = 300, external), and HANCOCK HNSCC (n = 763)—against nine baselines under 5-fold stratified cross-validation, with ablation across ten configurations. FRAE ranks first by MCC in D2 (0.587 vs. CAWPE 0.563) and second in D1 and D3, with the highest sensitivity in D3 (0.766). Ablation confirms that GL feature augmentation, not weight allocation, is the primary performance driver, contributing a directionally consistent AUC increment of 0.82–1.41 percentage points across all three tasks. On the external cohort, FRAE achieves AUC = 0.8305 [95% CI: 0.7772, 0.8739], ranking first by F1 and NPV, and tied for first by MCC among ten models, though confidence intervals overlap across all model pairs. Multi-institutional prospective validation remains the necessary next step.

Class imbalance and class cardinality both affect multiclass classification, but their influence on probabilistic estimation has been less explored. This study examines these impacts using an e-commerce dataset. The study follows a four-stage methodology comprising classifier comparative evaluation, controlled class-cardinality analysis, validation using real categorical variables, and class-imbalance evaluation. Multiclass classification tasks are evaluated using Support Vector Machine, Gaussian Naive Bayes, Logistic Regression, Random Forest, and Decision Tree classifiers. Under five-fold cross-validation, performance is assessed using the macro-F1 score, log loss, and accuracy. Results show that macro-F1 score and accuracy decrease as class cardinality increases, causing greater classification difficulty. Tree-based models like Random Forest exhibit more balanced performance across classes. Gaussian Naive Bayes obtains the lowest log loss, indicating more accurate probability estimations. Class cardinality effects are isolated by varying the number of classes while keeping the features and classifier fixed. Increasing class cardinality reduced posterior confidence and increased entropy and log loss. Using real-time categorical variables, these trends are confirmed. Class imbalance primarily affects minority class performance, whereas class cardinality exerts a broader influence on probabilistic confidence and prediction uncertainty. The findings emphasize the necessity to consider class cardinality, class imbalance, and probabilistic metrics when evaluating multiclass classification models.

Bio-inspired optimization algorithms have become an effective class of techniques for addressing challenging continuous optimization problems. In this work, we introduce the Parallel Enzyme Action Optimization (PEAO) algorithm, a parallel bio-inspired optimization approach that incorporates a multi-strategy communication mechanism among cooperative subpopulations. The population is partitioned into multiple subpopulations that evolve concurrently, promoting a more effective exploration of the search space. Furthermore, PEAO integrates adaptive search factors, local search procedure and communication strategies to improve solution quality while reducing the risk of premature convergence. In addition, a K-means-based population initialization procedure and a convergence-driven stopping criterion based on successive improvements in the best objective-function value are incorporated to reduce unnecessary objective-function evaluations and improve the overall efficiency of the optimization process.