## 5 Conclusions  
In this paper, we propose DLWP loss, a new approach that enhances the training of DL-based FD systems on imbalanced datasets. Through DLWP loss, we apply logit weight vectors to the penultimate layer of a DNN, introducing relevant perturbations meant to influence the network output strategically. In particular, we implement a training regime that facilitates the switching between logit vectors to help the classifier focus on samples from the minority classes while still effectively generalizing the entire dataset. The logit weight vectors as constituted act as network hyperparameters adjusted on a case-by-case basis to regulate the focus accorded to the various minority classes during training. For safety-related FD systems, we reduce the likelihood of predicting false negatives by generating models that prioritize recall, ensuring the bulk of positive samples are classified correctly despite the possible drawback of encountering a few incorrect predictions. Consequently, we apply a data-informed call to action, invoking human experts for feedback on a subset of the results, the model predictions with high uncertainties exceeding some preset threshold. Our results show that DLWP loss outperforms SOTA methods on the metrics Recall, ROC AUC, and per-class accuracy. We then extend our analysis to results from further experiments in which we reveal the effects of applying different reweighting and derivation strategies for the logit weight vector. In particular, our newly proposed reweighting strategy, the RLPr, shows improved results even for the SOTA methods. Finally, training on imbalanced datasets with DLWP loss eliminates the unintended additional workload or loss of data that comes with data re-sampling techniques. In the future, we aim to extend our study to include a formula that connects the generated logit weight vector to the precise level of imbalance in datasets. Additionally, we aim to improve our method by applying methods providing broader reasoning around uncertainty, such as ensemble-based methods and Bayesian models. Figure 6: Misclassification costs dispersion for different categories of methods evaluated on the APS Failure at Scania Trucks dataset. **Author Contributions:** Conceptualization, J.K., M.I.A. and J.G.B. ; methodology, J.K.; validation, J.K., M.I.A. ; formal analysis, J.K.; investigation, J.K.; writing--original draft preparation, J.K.; supervision, M.I.A. and J.G.B. ; project administration, M.I.A. ; funding acquisition, M.I.A. and J.G.B. All authors have read and agreed to the published version of the manuscript. **Funding:** This publication has emanated from research supported in part by a grant from Science Foundation Ireland under Grant Number SFI/16/RC/3918 (Confirm) and also by a grant from SFI under Grant Number SFI 12/RC/2289_P2 (Insight). For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data used for this study are publicly available and can be downloaded at [[https://archive.ics.uci.edu/ml/datasets/APS+Failure+at+Scania+Trucks](https://archive.ics.uci.edu/ml/datasets/APS+Failure+at+Scania+Trucks)]([https://archive.ics.uci.edu/ml/datasets/APS+Failure+at+Scania+Trucks](https://archive.ics.uci.edu/ml/datasets/APS+Failure+at+Scania+Trucks)) (APS Failure at Scania Trucks Data Set) and [[https://archive.ics.uci.edu/ml/datasets/Steel+Plates+Faults](https://archive.ics.uci.edu/ml/datasets/Steel+Plates+Faults)]([https://archive.ics.uci.edu/ml/datasets/Steel+Plates+Faults](https://archive.ics.uci.edu/ml/datasets/Steel+Plates+Faults)) (Steel Plates Faults Data Set) (accessed on 1 March 2021). **Conflicts of Interest:** The authors declare no conflict of interest.