# Automatic Optimization of Deep Learning Training through Feature-Aware-Based Dataset Splitting
## 1 Introduction  
In recent years, the proliferation of classification-capable artificial intelligence (AI) across a wide range of domains (e.g., civil engineering [1], agriculture [2], medicine [3, 4, 5], aeronautics [6], footwear retail [7], etc.) has been of outstanding importance for the digitalization of knowledge among vanishing professionals with particular sets of relevant skills, leading to the automation of workflows, time-effective decision support, and the dynamization of business models, among many other benefits. Supporting the success of this family of computational approaches are several artificial neural network architectures of distinctcomplexities that have been proposed over time by the technical-scientific community--e.g., VGG [8], ResNet [9], Xception [10], within the scope of deep learning--with the goal of providing different strategies to reach inference models of increasingly better performances, which are typically assessed against unseen data [11]. Among the perceived challenges is the need to mitigate potential overfitting, which affects models of higher complexity more severely [12]. Therefore, employing best-practices to find the proper balance between generalization capabilities and biases contributes to the achievement of more suitable inference capabilities. One of these practices consists of finding adequate training data arrangements to ensure harmonization, configured by a balanced distribution of examples per class/label, representativeness, diversity, among other aspects. In that regard, the data splitting process for setting up datasets is crucial for the achievement of reliable and consistent models [13]. In the dataset splitting context, there are different techniques [14]. Currently, two methods are widely used for this purpose: K-fold cross-validation (KFCV) and hold-out validation (HO) [15; 16; 17; 18]. These traditional approaches pose some issues, not only in terms of demanding both computational time and processing resources, but also because they do not always ensure the exploration of models' full inference potential (or nearly close to that golden rate). Other eventual drawbacks include decompensations that may prevent the matching of features between training and validation examples, and even lead to unrepresentative performance assessments while testing the models against unseen data, especially in cases wherein heterogeneity is a prevalent characteristic--for example, with the inclusion of many classes and/or noisy backgrounds. In cases wherein the datasets are reduced, the division can result in subsets composed of only a few samples, impacting the effectiveness of training or evaluation. If the dataset is unbalanced, and if certain classes or categories are underrepresented in the training, validation, or testing subset, the model may not learn accurately. Design patterns [19] may not work in such scenarios either. While rebalancing by oversampling may induce bias for decompensated classes, doing it by underfitting can lead to a substantial loss of problem representation. In this study, in contrast with conventional dataset split methodologies, two one-shot techniques are proposed and compared. Their primary focus is on achieving feature-based balanced datasets, aiming to mitigate the trial-and-error orientation underlying traditional subdivision approaches. More specifically, a lightweight feature-aware exploration strategy was conceptualized and developed to automatically and expeditiously organize data towards achieving uniform feature distribution, with the particular goal of assessing the impact of evenly distributing diversity in imagery characteristics on deep learning (DL) models' convergence. Tests made to evaluate the proposed strategies, considering two datasets with distinct contexts (grapevine leaves for phenotyping determination and bridge defects for the identification of degradation factors) demonstrated very promising results in comparison to the KFCV and HO techniques.