用协同进化提升极低标签数据下的表格分类性能
Cooperative Coevolution versus Monolithic Evolutionary Search for Semi-Supervised Tabular Classification
- 设计双视图特征子集与伪标签策略协同进化框架
- 在1%标签数据下相比轻量基线提升显著，中位MacroF1更高
- 适合低资源场景的表格分类研究者参考
本文研究极端低标签情形下的半监督表格分类问题，采用轻量级基础学习器。提出一种协同进化方法（CC-SSL），同时演化两个特征子集视图和一种伪标签策略，并与匹配的单体进化基线（EA-SSL）及三种轻量级半监督基线进行对比。在25个OpenML数据集上，以1%、5%、10%标签比例评估测试集宏平均F1和准确率，辅以进化过程与伪标签诊断。结果显示，CC-SSL与EA-SSL的中位测试宏平均F1均优于轻量基线，尤其在1%标签时差距最大。多数情况下，两者最终测试性能无显著差异。EA-SSL在搜索过程中表现出更高的适应度值和多样性，达到目标时间相近，多分类设置下代数到目标更优。伪标签数量、ProbeDrop及验证乐观性在相同协议下，两方法无显著差异。
原文摘要 · Abstract (English)
This paper studies semi-supervised tabular classification in the extreme low-label regime using lightweight base learners. The paper proposes a cooperative coevolutionary method (CC-SSL) that evolves (i) two feature-subset views and (ii) a pseudo-labeling policy, and compares it to a matched monolithic evolutionary baseline (EA-SSL) and three lightweight SSL baselines. Experiments on 25 OpenML datasets with labeled fractions {1%,5%,10%} evaluate test MacroF1 and accuracy, together with evolutionary and pseudo-label diagnostics. CC-SSL and EA-SSL achieve higher median test MacroF1 than the lightweight baselines, with the largest separations at 1% labeled data. Most CC-SSL vs. EA-SSL comparisons are statistical draws on final test performance. EA-SSL shows higher best-so-far fitness and higher diversity during search, while time-to-target is comparable and generations-to-target favors EA-SSL in several multiclass settings. Pseudo-label volume, ProbeDrop, and validation optimism show no significant differences between CC-SSL and EA-SSL under the shared protocol.
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