Optimized Sequential Testing for Binary Ensemble Classifiers

2026-06-13

AI Summary

This work proposes a sequential testing–based early-stopping strategy for binary ensemble classifiers to reduce inference overhead while strictly bounding the divergence rate from predictions of the full ensemble. The approach terminates evaluation as soon as a decisive majority emerges during the sequential assessment of base models. Under three optimality criteria, the strategy can be formulated as a linear programming problem, enabling efficient computation of the optimal stopping rule. Experimental results on UCI and Grinsztajn benchmark datasets demonstrate that the method achieves an average speedup exceeding 4× while consistently maintaining prediction divergence below 0.1%.

Abstract

Ensemble classifiers are predictive models that combine the results of simpler base models, often by majority vote. A classic example is random forests, which combine the predictions of decision trees. Ensembles that use more base models can be more accurate but also more costly to train and run. In this paper, we consider strategies for reducing the computational cost of binary classification using an approach from the field of sequential testing. Rather than evaluating all the base models and taking a majority vote, we evaluate the base models sequentially and stop execution when a clear majority emerges. We consider three different notions of optimality for early-stopping strategies that minimize the number of base models executed while controlling the rate of disagreement with the full ensemble. For each notion of optimality and allowable disagreement rate, we show that a linear program can be constructed and solved efficiently to find the optimal stopping strategy. We tested these methods on real-world datasets taken from the UC Irvine Machine Learning repository, and on the benchmark datasets proposed by Grinsztajn et al. We found that on most datasets, these methods provide speed-ups of 4x or more while controlling disagreement at 0.1%

Problem

• ensemble classifiers
• sequential testing
• computational cost
• early stopping
• binary classification

Innovation

• sequential testing
• ensemble classifiers
• early stopping
• linear programming
• computational efficiency