Enhancing Pairwise Comparison Classification with a Noise Reduction Mechanism Using Denoising Auto-encoders

Abstract

Pairwise comparison classification models are widely recognized for their utility in ranking, recommendation systems, and preference learning, where relative confidence between data points holds more significance than absolute labels. These models provide a robust alternative to traditional classification methods, particularly in settings where labels are limited, ambiguous, or noisy. However, the performance of Pcomp models is highly sensitive to label noise, such as miss-ordered or misclassified pairs, which can distort learning signals and degrade model accuracy and stability.This research introduces an innovative framework that integrates a noise reduction mechanism using denoising auto-encoders (DAEs) to preprocess noisy pairwise data. By reconstructing clean inputs from noisy pairs, the DAE enhances data quality, enabling the Pcomp classifier to focus on meaningful relationships. Experiments conducted on benchmark datasets including MNIST, Fashion-MNIST, CIFAR-10, Kuzushiji-MNIST, and UCI datasets (USPS, Pendigits, and Optdigits) demonstrate that the proposed method significantly improves accuracy and robustness in noisy environments, achieving performance gains of up to 15%. This study provides a scalable and robust solution for real-world applications where noisy data is prevalent, extending the applicability of Pcomp models to domains requiring resilience against data imperfections.

Introduction

The study addresses the challenge of label noise in supervised machine learning, particularly in pairwise comparison classification (Pcomp), which ranks or compares data pairs rather than relying on absolute labels. Label noise—caused by annotation errors or ambiguous data—can severely degrade model performance. While Pcomp is useful in applications like recommendation systems where relative preferences are more accessible than exact labels, it remains sensitive to noisy, mislabeled pairs.

To tackle this, the paper proposes integrating denoising autoencoders (DAEs), a neural network architecture known for filtering noise by reconstructing clean data from corrupted inputs, into the Pcomp framework. This integration preprocesses noisy pairwise data to improve quality before classification. Additionally, a modified empirical risk estimator with a ReLU-based correction is introduced to reduce residual noise effects during training.

The method involves:

• Generating noisy pairwise data by flipping or reversing labels,

• Training a DAE to reconstruct clean data,

• Feeding denoised data into a Pcomp classifier optimized with noise-corrected risk estimation.

The approach is evaluated on multiple benchmark datasets (including MNIST, CIFAR-10, and UCI datasets) converted into binary classification tasks with artificial noise. Experimental results show significant improvements in accuracy and robustness under different noise and class prior conditions.

Conclusion

This study introduces a noise reduction mechanism using a denoising autoencoder to enhance pairwise comparison classification. The proposed approach significantly improves accuracy and robustness, particularly in noisy environments where traditional Pcomp methods struggle. The findings validate the utility of denoising auto-encoders in reducing noise impact, offering a scalable solution for real-world applications involving noisy data. Future work will explore adaptive noise filtering techniques and broader applications in multi-class classification and complex datasets.

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Copyright

Copyright © 2025 Waqar Asghar, Ahmad Mushtaq. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.