# Drilling Condition Identification Method for Imbalanced Datasets
###### Abstract
Consequently, the model tends to favor majority-class conditions during prediction, reducing its reliability in identifying minority-class conditions. Although the overall classification accuracy of the model may be high, it fails to accurately recognize all types of drilling conditions. This diminishes the effectiveness of the recognition results as a reliable basis for assessing drilling status, ultimately limiting the practical value of the model in real-world applications [7]. Class imbalance is a common issue in classification prediction tasks. To address this problem, relevant scholars have conducted targeted research on how to eliminate the impact of imbalanced data on classification prediction tasks, focusing on areas such as data preprocessing, classifier construction, and classifier evaluation [8,9]. In data preprocessing, commonly used methods include undersampling [10], oversampling [11], and sample augmentation techniques based on generative models [12]. Undersampling balances the data distribution by reducing the number of majority-class samples. Although this method can significantly improve classifier performance, it may result in the loss of useful information [13]. On the other hand, oversampling addresses the class imbalance by increasing the number of minority-class samples, but it is important to avoid the risk of overfitting due to excessive replication [14]. The application of generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) has provided new pathways for generating high-quality minority-class samples [15,16]. In terms of classifier construction, scholars have explored strategies such as cost-sensitive learning and ensemble learning [17]. Cost-sensitive learning introduces class weights or designs adaptive loss functions to place more emphasis on the correct classification of the minority class during training [18]. Ensemble learning methods, such as Bagging and Boosting, enhance the robustness and generalization ability of classification models by combining the decisions of multiple weak classifiers, thus improving performance on imbalanced datasets [19,20]. Regarding classifier evaluation, traditional accuracy metrics are no longer effective in reflecting the actual performance of models on imbalanced datasets. As a result, metrics such as the \(F1\) score and the Area Under the ROC Curve (AUC) are widely used to provide a more comprehensive evaluation of the model's performance across all classes [21]. Although improvements in classifier construction and evaluation methods provide important tools for addressing the class imbalance issue, effectively mitigating this problem at its core still relies on data preprocessing. Data preprocessing methods, due to their model-independent nature and flexibility in optimizing sampling strategies and feature space representations, have become the preferred solution for addressing class imbalance in many fields. However, research in the field of drilling condition identification remains somewhat limited. With the development of intelligent drilling technologies, introducing these methods into the processing and modeling of drilling condition data could potentially offer new breakthroughs in improving model performance and reliability. To address this, this paper proposes a drilling condition identification method that integrates feature engineering, data resampling, and deep learning model optimization. A weighted symmetric uncertainty method is employed to prioritize features that are beneficial for recognizing the minority class, enhancing the model's ability to identify minority classes and balancing classification performance. A sliding-window-based SMOTE over-sampling method is introduced, which processes the data in segments to preserve temporal dependencies and generates a balanced dataset that follows the time evolution pattern, thereby addressing the issue of insufficient minority-class samples.