Ensemble Based Multi Class Classification With Bootstrap Aggregation

via “ensemble-based multi-class classification with bootstrap aggregation”

* 🏆 2001: [A fast and elitist multiobjective genetic algorithm (NSGA-II)](https://ieeexplore.ieee.org/abstract/document/996017)

Unique: Uses random feature subsets at each split (not just random samples) to decorrelate trees, combined with maximum-depth growth and no pruning — this specific combination of randomization sources (data + features) is more effective at variance reduction than single-source randomization used in earlier ensemble methods

vs others: Outperforms single decision trees by 10-30% on typical tabular datasets due to variance reduction through decorrelation, while remaining faster to train than gradient boosting methods and requiring less hyperparameter tuning than neural networks

via “classification accuracy improvement via majority voting aggregation”

* 🏆 1998: [Gradient-based learning applied to document recognition (CNN/GTN)](https://ieeexplore.ieee.org/abstract/document/726791)

Unique: Applies simple plurality voting without confidence weighting or adaptive aggregation, relying on error decorrelation from bootstrap resampling to achieve accuracy gains — a theoretically grounded approach that contrasts with weighted voting schemes by treating all ensemble members equally and depending entirely on bootstrap-induced diversity

vs others: Simpler than weighted voting or stacking (no meta-learner required) and more interpretable than neural network ensembles, but less adaptive than boosting-based methods that explicitly weight classifiers by accuracy