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International Conference on Electronic Information Engineering and Computer Science (EIECS)

Construction of Weak Classifiers and Optimized Implementation of QBoost Ensemble Model

Jiarui Liu, X.H. Wu, Yiwei Liao, Hui Peng

Published Sep 26, 2025• 0 citations
Publisher

Abstract

QBoost, a quantum-inspired ensemble learning algorithm, has performance that depends on the quality of weak classifiers and efficiency of the optimization process. Existing methods suffer from insufficient diversity among weak classifiers and low efficiency in handling nonlinear data patterns, leading to unsatisfactory generalization ability. This study proposes a new weak classifier construction method based on pruned random decision trees-combined with a multi-objective optimized QBoost model-to fix these problems. Weak classifiers are constructed using random subspaces with adaptive tree pruning; this enhances diversity while cutting overfitting. For QBoost, the study introduces a multi-objective optimization framework to minimize classification loss and maximize weak classifier diversity at the same time, and uses an improved AdamW algorithm to speed up convergence. Experiments on KDD Cup 99, IMDb, and Covertype datasets show the proposed method hits $96.8 \%$ classification accuracy on KDD Cup 99- $\mathbf{3. 3 \%}$ higher than Gradient Boosting ($93.5 \%$) and $2.6 \%$ higher than standard QBoost ($94.2 \%$). Compared with standard QBoost, its training time drops by $37 \%$, and it keeps $89.7 \%$ accuracy under $15 \%$ feature noise-proving better robustness. These findings confirm the proposed method effectively improves classification performance, efficiency, and diversity.

DOI
10.1109/eiecs67708.2025.11283264

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Ensemble learning algorithms, especially Boosting, have achieved remarkable success in classification tasks by combining multiple weak classifiers-you know, the sort that aren’t all that strong by themselves but work real well when you put them together-though traditional methods like AdaBoost? They’re prone to overfitting in noisy datasets, see? Like when a few labels are off or the data has random little glitches, they end up fitting that noise too hard instead of the real pattern, and methods like QBoost-the ones that use quantuminspired optimization, right?-they usually run into two problems: picking the right weak classifiers and just not being very fast computationally, so this study? We propose an optimized QBoost version plus a new way to build weak classifiers, just to fix those issues we mentioned, and the weak classifiers here? We made them based on decision stumps but added adaptive feature weighting, and that weighting part, by the way, helps make the weak classifiers more diverse and better at telling different classes apart, while as for the QBoost framework itself? We tweaked the objective function-threw in a regularization term, yeah?-to balance two things: getting solid classification accuracy and not letting the model get too complex, and we also used a gradient-based optimization algorithm to speed up convergence so the model doesn’t take forever to train; now, the experimental results?We tested on MNIST, CIFAR-10, and UCI Credit. The method shows 3.2% higher accuracy than AdaBoost.It cuts training time by 28%. In label-noise settings, the error rate falls by 5.7%.
We introduce a novel discrete optimization method for training in the context of a boosting framework for large scale binary classifiers. The motivation is to cast the training problem into the format required by existing adiabatic quantum hardware. First we provide theoretical arguments concerning the transformation of an originally continuous optimization problem into one with discrete variables of low bit depth. Next we propose QBoost as an iterative training algorithm in which a subset of weak classifiers is selected by solving a hard optimization problem in each iteration. A strong classifier is incrementally constructed by concatenating the subsets of weak classifiers. We supplement the findings with experiments on one synthetic and two natural data sets and compare against the performance of existing boosting algorithms. Finally, by conducting a quantum Monte Carlo simulation we gather evidence that adiabatic quantum optimization is able to handle the discrete optimization problems generated by QBoost.
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ABSTRACT Ensemble learning plays a central role in improving the generalization of machine learning models, yet its full potential remains underexplored in the quantum domain. In this work, we propose a Quantum Ensemble Learning (QEL) algorithm that leverages the parallelism of quantum computing to construct diverse classifiers in a shallow‐circuit ensemble. The method uses a control‐qubit‐based sampling strategy to create superpositions of training subsamples, enabling simultaneous evaluation of multiple weak learners. The algorithm proceeds through four stages: quantum state preparation, superposition‐based sampling, learning via quantum interference, and final measurement. Our framework is flexible and allows different quantum classifiers to be used as base learners. We demonstrate this by implementing a robust QSVM‐based ensemble with quantum feature extraction using PauliFeatureMaps. Unlike classical methods, this technique adds classifiers to time complexity additively rather than multiplicatively due to the parallelism of quantum computing. To demonstrate the workflow of the proposed algorithm, we include a step‐by‐step example using the XOR problem. Simulation results further validate the effectiveness of the QEL framework—enhanced by the integration of a Quantum Support Vector Machine (QSVM) classifier with quantum feature extraction—showing improved classification accuracy compared to existing algorithms across several UCI benchmark datasets using IBM Qiskit.
Quantum machine learning has shown advantages in many ways compared to classical machine learning. In machine learning, a difficult problem is how to learn a model with high robustness and strong generalization ability from a limited feature space. Combining multiple models as base learners, ensemble learning (EL) can effectively improve the accuracy, generalization ability and robustness of the final model. The key to EL lies in two aspects, the performance of base learners and the choice of the combination strategy. Recently, quantum EL (QEL) has been studied. However, existing combination strategies in QEL are inadequate in considering the accuracy and variance among base learners. This paper presents a hybrid EL framework that combines quantum and classical advantages. More importantly, we propose an efficient combination strategy for improving the accuracy of classification in the framework. We verify the feasibility and efficiency of our framework and strategy by using the MNIST dataset. Simulation results show that the hybrid EL framework with our combination strategy not only has a higher accuracy and lower variance than the single model without the ensemble, but also has a better accuracy than the majority voting and the weighted voting strategies in most cases.
Abstract In ensemble learning, random subspace technology not only easily loses some important features but also easily produces some redundant subspaces, inevitably leading to the decline of ensemble learning performance. In order to overcome the shortcomings, we propose a new selective quantum ensemble learning model inspired by improved AdaBoost based on local sample information (SELA). Firstly, SELA combines information entropy and random subspace to ensure that the important features of the classification task in each subspace are preserved. Then, we select the base classifier that can balance accuracy and diversity among a group of base classifiers generated based on local AdaBoost in each iteration. Finally, we utilize the quantum genetic algorithm to search optimal weights for base learners in the label prediction process. We use UCI datasets to analyze the impact of important parameters in SELA on classification performance and verify that SELA is usually superior to other competitive algorithms.