Cooperative Coevolution versus Monolithic Evolutionary Search for Semi-Supervised Tabular Classification

Smart-city applications often combine scarce labeled data with abundant unlabeled observations. Semi-supervised learning (SSL) exploits both labeled and unlabeled data to improve classification under limited supervision. This article presents MOEA-SSL, a multi-objective evolutionary approach for lightweight semi-supervised tabular classification that jointly optimizes predictive quality and feature usage. Experimental evaluation on three Smart City classification datasets, considering 1%, 5%, and 10% labeled data, shows that MOEA-SSL achieves competitive predictive performance compared with established lightweight SSL methods while significantly reducing feature usage relative to EA-SSL in all evaluated conditions. The computed Pareto fronts also provide lower-feature-use alternatives with limited degradation in classification performance.

In this work, we conduct an empirical study on self-supervised learning (SSL) for tabular data. Our experiments examine limited label regimes (that is, 1%, 5%, 10%, and 20% of the labels) and highlight limitations and opportunities in current tabular SSL. Contrary to what is expected, our mask-and-recover approach shows its largest improvements for datasets that have little to no recorded numeric missingness, whereas it more often leads to degradation on datasets that are characterized by native numeric missingness (post hoc split). Random Forest achieves an average AUC of 0.9015 across the 14 tasks for the 10% labels setting, whereas SSL shows a performance of ~0.8954 (RF: 1 draw; SSL: 3-draw mean). Moreover, the gain of SSL compared to training from scratch exhibits a high variance across tasks and is not significant (1 run; Wilcoxon p = 0.626, Holm-adjusted p = 1.000, for both the 5% and 10% settings). On the other hand, we observe that SSL-pretrained models achieve a higher average AUC when MNAR and MCAR missingness were injected at test-time. In comparison to models trained from scratch, our SSL-pretrained models show an improvement of +0.0245 (MCAR) and +0.0418 AUC (MNAR) under test-time missingness (single seed), with 11 (MCAR; plus one tie) and 8 (MNAR) positive results across the 14 tasks. However, after Holm-Bonferroni correction neither improvement is significant (Holm-adjusted p = 0.118 for MCAR and 0.518 for MNAR). Finally, when comparing mask-and-recover with three other approaches, namely, simplified VIME, SCARF and SubTab implementations using an identical encoder, we observe no significant difference in performance (Holm-adjusted p = 0.459 for VIME, 1.000 for SCARF, 1.000 for SubTab).

Background. Labeled data for security classification is scarce. Semi-supervised learning (SSL) propagates labels from a small labeled pool to larger unlabeled pools. Yet security applications often use SSL as a black box: default parameters, a fixed classifier, and no handling of pseudo-label-induced class imbalance. Aims. Recent work reports sizeable gains from optimizing SSL pipelines via joint search, AutoML, or per-component tuning. These gains are hard to attribute: they may reflect useful SSL-classifier interactions, or mostly from simply tuning the downstream classifier. We disentangle these effects for binary tabular security data with classical SSL and tree-based classifiers. Method. We build SemiScope as an analysis instrument, not a deployment recommendation. It uses Bayesian Optimization to jointly tune SSL settings, confidence filtering, oversampling, and the classifier. The key control, Tuned-Clf, fixes SSL to defaults but gets the same 100-trial classifier budget and validation-set threshold tuning as SemiScope. At 10% labels, we compare them with paired TOST using a +/-1.0 g-measure smallest effect of interest. Results. SemiScope beats every default SSL baseline on all five datasets, improving over the strongest by 0.7-12.7 points. Under the equal-budget control, Tuned-Clf is statistically equivalent to the full pipeline on 4 of 5 datasets; Phishing is inconclusive. Classifier HPO alone recovers a median 86% of SemiScope's gain over Default Self-Training (ST) + Random Forest (RF). Conclusions. The reusable contribution is the decomposition protocol. A simpler recipe suffices: use Self-Training, tune the classifier with Bayesian Optimization, and tune the decision threshold on validation data. It reaches within 1 g-measure of Supervised RF at 20-30% labels on four datasets and 40% on Drebin, at the same or lower label rate than Default ST + RF on every dataset.