We predict weekly telephony contact for Centrelink customers using longitudinal administrative data aligned to a weekly grid, comparing statistical, machine learning, and deep learning models on 889,879 customers (150 features; 3.65M observations after class balancing; 5-fold CV). Feature-engineering regimes spanned from quick encodings to contact-history windows of 1, 4, 8, and 12 weeks; an 8-week history yielded the best trade-off, with diminishing returns beyond 8 weeks. Across 12 models, a Multilayer Perceptron attained the strongest performance on held-out data (AUC ≈ 0.84; accuracy ≈ 0.79). One-factor-at-a-time hyperparameter searches produced modest, non-monotonic gains, underscoring that additional tuning effort does not necessarily translate to uplift in model performance. To guide modelling investment and effort decisions, we introduced a cost-based budgeting equation associated with treatment applications of the predictive model. This framework approach encourages monetary costings to be considered alongside predictive performance for model development.
Software vulnerabilities are referred to as weaknesses in the source code. Hackers exploit these weaknesses to perform malicious actions, including accessing sensitive data and injecting a computer virus to hijack the computer system. Identifying these vulnerabilities is challenging. Even a perfectly functional program may have hidden vulnerable patterns. Contrastingly, locating these patterns manually by cybersecurity experts is onerous and time-consuming. Existing research uses deep learning algorithms to automate the process of finding vulnerable patterns. However, extracting relevant features from the source code is challenging, as source code can exceed hundreds of lines. Existing researchers either extract individual functions in a program or encapsulate the entire program, leading to under- or over-representation, respectively. A recent notion of using program slices has emerged, which only retains the most likely vulnerabilities causing statements. However, no significant research has been conducted to identify the most significant characteristics that are likely to cause vulnerabilities. Therefore, in this research, we investigate statistical heuristics to dynamically determine the most representative vulnerability characteristics and propose a deep neural network based on RoBERTa embeddings, which is fine-tuned on CodeBERT using multi-sample dropout to enhance generalizability. Our experimental results on real-world software source databases show up to 37
In deep learning-based image classification, it is necessary to achieve high generalization performance while suppressing overfitting in situations when training data are limited. Mixup, a data augmentation using linear interpolation in the input space, is effective in smoothing the decision boundary, but it can hinder the model’s ability to learn fine-grained, class-specific details in the later training stages. On the other hand, augmentation by First-Order Manifold Augmentation (FOMA) utilizes local geometry but is designed for regression, making its direct application to classification difficult. We propose a two-phase augmentation method that changes its strategy according to the phase of training. In Phase 1, standard Mixup is used for global interpolation to smooth the class boundary. In Phase 2, we apply Local-FOMA, which is designed for classification, to estimate principal component directions based on local neighborhoods in the feature space and perform data augmentation while preserving the manifold structure. The weights of the loss function are linearly transitioned during the learning process, enabling a smooth switch from Mixup to Local-FOMA. On CIFAR-100 and CIFAR-10, our method outperformed strong Mixup variants. Under distribution shifts (CIFAR-C), it also improves robustness and was particularly effective on CIFAR-100-C, suggesting that the method is particularly advantageous in complex scenarios involving datasets with many classes. These results demonstrate that Mixup-FOMA provides an effective strategy to enhance both classification performance and robustness by gradually combining global generalization with local refinement.
Forecasting stock market indices is challenging due to the dynamic and non-stationary nature of financial data. Traditional machine learning methods often struggle to capture these complexities, limiting their accuracy. In this paper, we propose two novel dynamic meta-learning ensembles—DME-PC and DME-HC, to address the critical challenge of ensemble member selection and adapting the prediction to each predicted data point, to enhance accuracy. To select ensemble members, DME-PC employs pairwise comparisons with statistical testing while DME-HC uses hierarchical clustering. To adapt the ensemble to the characteristics of the predicted data points, DME-PC and DME-HC combine dynamically the ensemble member predictions based on their performance on recent data and predicted future performance. Experiments on four financial datasets for 10 years and comparison with statistical, machine learning and deep learning methods showed the superior performance of the proposed dynamic ensembles. The best results were obtained with DME-HC (EWA), which utilizes hierarchical clustering for ensemble member selection and exponentially weighted average on most recent errors. Our results highlight the potential of the proposed dynamic ensembles to effectively capture temporal patterns in financial data and support better investment decisions.
Multi-omics data, encompassing heterogeneous sources such as genomics, transcriptomics, and proteomics, has become a powerful resource for disease classification in bioinformatics. Fusion mechanisms combine features from multiple modalities into a single joint representation for predictive models to improve classification performance. However, while most studies focus on accuracy, model calibration - how well predicted probabilities match actual outcomes - remains underexplored in the multi-omics setting. Poor calibration can make even accurate models overconfident and unreliable. This study investigates the impact of three fusion strategies, early, intermediate and late, on three state-of-the-art post-hoc calibration techniques. We further introduce dynamic scaling, a regression-based calibration method that estimates instance-specific temperature parameters for more adaptive calibration than dataset-level techniques. Experiments on four benchmark omics datasets evaluate both classification and calibration metrics. Results show that data quality and class imbalance strongly influence performance, and while dynamic scaling often achieves the best calibration, challenges persist for small and imbalanced datasets.
