发表机构
United Arab Emirates University; Abdul Wali Khan University Mardan（阿联酋大学; 阿卜杜勒·瓦利·汗大学马尔丹分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出ARISE方法，整合多类特征选择相关机制，在多组学数据集的小样本分子分类任务中，相比对比方法在多项指标上均取得最优表现。
AI 中文摘要
目标：小样本分子分类需要特征选择器，以识别针对二分类和多分类结果的具有预测性、稳定性且非冗余的特征子集。我们提出ARISE（Adaptive Residual-Informed Stability Ensemble，自适应残差感知稳定性集成），该方法整合了互补相关性信号、类别平衡稳定性评估、残差感知冗余控制以及多分类成对覆盖。方法：ARISE通过15个预定义配置文件组合7个经百分位数归一化的相关性组件，采用嵌套内部交叉验证进行自适应加权。在5个分子数据集、8种特征集规模、3种固定分类器（k近邻、支持向量机、随机森林）以及6种过滤式对比方法上对其进行评估。采用50次重复的5折外部交叉验证，使用平衡准确率、宏F1分数和Cohen's kappa来评估泛化性能。结果：在210,000次保留评估中，ARISE在全部15个数据集-指标组合中均排名第一。同数据集均值的平衡准确率为0.793，宏F1分数为0.776，kappa为0.725，分别比最强的聚合对比方法高出0.022、0.023和0.028。在紧凑特征集上性能依然强劲，不过最优预算因数据集而异。结论：ARISE提供了一个透明的自适应框架，可同时解决相关性、稳定性、冗余性和多分类判别问题。其在数据集、分类器、指标和特征集规模上的一致结果，支持对其在小样本分子分类中的进一步评估。
英文摘要
Objective: Small-sample molecular classification requires feature selectors that identify predictive, stable, and nonredundant subsets for binary and multiclass outcomes. We propose ARISE (Adaptive Residual-Informed Stability Ensemble), which integrates complementary relevance signals, class-balanced stability assessment, residual-informed redundancy control, and multiclass pairwise coverage. Methods: ARISE combines seven percentile-normalized relevance components through 15 predefined profiles, adaptively weighted by nested inner cross-validation. It was evaluated on five molecular datasets, eight feature-set sizes, three fixed classifiers (k-nearest neighbours, support vector machine, and random forest), and six filter comparators. Generalization was estimated by five-fold outer cross-validation repeated 50 times using balanced accuracy, macro-F1, and Cohen's kappa. Results: Across 210,000 held-out assessments, ARISE ranked first in all 15 dataset-metric combinations. Equal-dataset means were 0.793 for balanced accuracy, 0.776 for macro-F1, and 0.725 for kappa, exceeding the strongest aggregate comparator by 0.022, 0.023, and 0.028, respectively. Performance remained strong across compact feature sets, although the optimal budget differed by dataset. Conclusion: ARISE provides a transparent, adaptive framework that jointly addresses relevance, stability, redundancy, and multiclass discrimination. Its consistent results across datasets, classifiers, metrics, and feature-set sizes support further evaluation for small-sample molecular classification.
Comments30 pages, 6 figures
