arXiv subjects

Lynn Houthuys

Publications and source records attributed to Lynn Houthuys.

4 recordsLinked to original sources

arXiv2026

Adaptive Weighted LSSVM for Multi-View Classification

Multi-view learning integrates diverse representations of the same instances and can improve performance when interactions across views are effectively exploited. Most existing kernel-based multi-view learning methods either rely on fusion techniques without explicitly enforcing a consensus or complementary collaboration across views, or use co-regularization-based loss functions that impose only pairwise interactions, thereby limiting global collaboration. We propose AW-LSSVM, an adaptive weighted LS-SVM that explicitly enforces complementary learning across all views through an iterative global coupling mechanism. At each iteration, each view not only learns from its own data but is also guided to compensate for samples misclassified by other views in previous iterations by assigning adaptive sample weights. We introduce two strategies for computing these weights: (1) based on averaging misclassification errors across other views and, (2) based on a dissimilarity-aware error aggregation that puts more emphasis on errors from more dissimilar views. Experiments demonstrate that AW-LSSVM outperforms existing multi-view methods on most benchmark datasets.

cs.LG↗

arXiv2026

Evaluating and Mitigating Gender Bias in Pre-trained Embeddings for ML-based Recruitment

AI-based recruitment systems that rely on machine learning models trained on historical CV data, risk perpetuating and amplifying social biases. A key challenge arises in unstructured CV text, where pre-trained language model embeddings may infer sensitive attributes such as gender even after explicit indicators are removed. In this paper, we evaluate nine pre-trained embedding models on the synthetic FairCVdb dataset, analyzing the informativeness of their embeddings for applicant scoring and their susceptibility to gender leakage, on both original and gender-scrubbed biographies. We further use a multi-task adversarial learning framework with gradient reversal to predict applicant suitability while suppressing gender information from learned representations. Finally, we use a multi-objective Pareto-front-based model selection to balance predictive utility and fairness. Our experimental results show that explicit gender scrubbing substantially reduces but does not eliminate gender leakage, while adversarial learning improves fairness mainly on original biographies and acts as a complementary strategy rather than a substitute for text-level debiasing.

cs.LG↗

arXiv2025

Multi-view mid fusion: a universal approach for learning in an HDLSS setting

The high-dimensional low-sample-size (HDLSS) setting presents significant challenges in various applications where the feature dimension far exceeds the number of available samples. This paper introduces a universal approach for learning in HDLSS setting using multi-view mid fusion techniques. It shows how existing mid fusion multi-view methods perform well in an HDLSS setting even if no inherent views are provided. Three view construction methods are proposed that split the high-dimensional feature vectors into smaller subsets, each representing a different view. Extensive experimental validation across model-types and learning tasks confirm the effectiveness and generalization of the approach. We believe the work in this paper lays the foundation for further research into the universal benefits of multi-view mid fusion learning.

cs.LG↗

arXiv2022

Machine learning for automated quality control in injection moulding manufacturing

Machine learning (ML) may improve and automate quality control (QC) in injection moulding manufacturing. As the labelling of extensive, real-world process data is costly, however, the use of simulated process data may offer a first step towards a successful implementation. In this study, simulated data was used to develop a predictive model for the product quality of an injection moulded sorting container. The achieved accuracy, specificity and sensitivity on the test set was $99.4\%$, $99.7\%$ and $94.7\%$, respectively. This study thus shows the potential of ML towards automated QC in injection moulding and encourages the extension to ML models trained on real-world data.

cs.LG↗