Selecting Near-Optimal Learners via Incremental Data Allocation
This addresses the challenge of managing large datasets and numerous classifier options in machine learning, offering an incremental improvement in data allocation efficiency.
The paper tackles the problem of efficiently selecting a near-optimal classifier by sequentially allocating small subsets of training data among many classifiers, aiming to minimize misallocation costs. It proposes the DAUB strategy, which achieves these objectives with theoretical sub-linear regret bounds and practical validation on real-world datasets.
We study a novel machine learning (ML) problem setting of sequentially allocating small subsets of training data amongst a large set of classifiers. The goal is to select a classifier that will give near-optimal accuracy when trained on all data, while also minimizing the cost of misallocated samples. This is motivated by large modern datasets and ML toolkits with many combinations of learning algorithms and hyper-parameters. Inspired by the principle of "optimism under uncertainty," we propose an innovative strategy, Data Allocation using Upper Bounds (DAUB), which robustly achieves these objectives across a variety of real-world datasets. We further develop substantial theoretical support for DAUB in an idealized setting where the expected accuracy of a classifier trained on $n$ samples can be known exactly. Under these conditions we establish a rigorous sub-linear bound on the regret of the approach (in terms of misallocated data), as well as a rigorous bound on suboptimality of the selected classifier. Our accuracy estimates using real-world datasets only entail mild violations of the theoretical scenario, suggesting that the practical behavior of DAUB is likely to approach the idealized behavior.