Cooperative Coevolution versus Monolithic Evolutionary Search for Semi-Supervised Tabular Classification
For practitioners needing semi-supervised tabular classification with very few labels, this work shows that evolutionary methods outperform lightweight SSL baselines, but cooperative coevolution does not significantly improve over monolithic evolution.
This paper proposes a cooperative coevolutionary method (CC-SSL) for semi-supervised tabular classification in the extreme low-label regime and compares it to a monolithic evolutionary baseline (EA-SSL) and lightweight SSL baselines. On 25 OpenML datasets, CC-SSL and EA-SSL achieve higher median test MacroF1 than lightweight baselines, with the largest gains at 1% labeled data, but most comparisons between CC-SSL and EA-SSL are statistical draws.
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.