Raul Quintana Baldovinos

While data scarcity is often cited as a major challenge in machine learning, the opposite problem—having too much data or an unlimited stream of incoming data—is equally relevant. In such cases, a key question arises: which examples are the most useful for training a model while avoiding excessive computational cost? This work addresses the problem by proposing a strategy to automatically select useful examples while preserving model effectiveness and training efficiency. We propose and evaluate a new strategy—Adding Only Errors (AOE)—to avoid using the full training set for classifier training. Our results show that when the training-validation split is properly sampled, the size of the training set can be reduced by more than three orders of magnitude without compromising performance. Although this reduction may initially incur higher computational costs, in real-world scenarios involving continuous streams of labeled data requiring frequent model retraining, AOE amortizes this cost by significantly decreasing dataset size.