## 1 Introduction
Feature selection methods can be categorized into filter methods (e.g., Chi-square test, Information Gain), wrapper methods (e.g., Recursive Feature Elimination), and embedded methods (e.g., LASSO regression) [6; 24; 25; 26; 27]. KNN is a supervised non-parametric instance-based learning algorithm commonly used for classification tasks. It assigns a class label to a new data point based on the majority vote of its K-Nearest Neighbors in the training data. The choice of K value and distance metric significantly impacts KNN performance [28]. Common KNN performance evaluation metrics include accuracy, precision, recall, F1-score, and AUC-ROC curves. Accuracy measures the proportion of correctly classified instances. Precision reflects the ratio of true positives among predicted positives, while recall indicates the proportion of true positives identified. F1-score is a harmonic mean of precision and recall, providing a balanced view of model performance. The AUC-ROC curve depicts the relationship between true positive rate and false positive rate, offering a comprehensive evaluation of the model's ability to discriminate between classes [29; 30; 31]. Furthermore, Random Forest is a common model that is used for feature reduction in supervised learning techniques, see, e.g., [32; 33]. Existing research demonstrates the effectiveness of combining unsupervised and supervised methods for data reduction, particularly in the context of high-dimensional data. Several studies utilize clustering for feature selection [22; 23]. These approaches often group features based on similarity and select representative features from each cluster. Additionally, clustering is employed before classification tasks [28; 34; 35]. For instance, K-means clustering can be used to label data for anomaly detection or network intrusion detection [36]. Beyond traditional dimensionality reduction techniques, recent advancements in deep learning have introduced powerful new approaches for handling high-dimensional data. Deep generative models, in particular, have shown remarkable capabilities in both feature extraction and dimensionality reduction while preserving essential data characteristics. Generative Adversarial Networks (GANs) represent one such innovation, where the interplay between generator and discriminator networks enables the learning of complex data distributions in lower-dimensional latent spaces. Research on LD-GAN [37] proposed a low-dimensional GAN specifically designed for spectral image generation that effectively reduces dimensionality while maintaining spectral fidelity. Variational Autoencoders (VAEs) offer another compelling approach, as they learn a probabilistic mapping between the original data space and a lower-dimensional latent representation. Studies on VAE applications in game design [38] have demonstrated their effectiveness in processing and transforming image data, where high-dimensional visual information is efficiently compressed while preserving key features necessary for interactive applications. The work on Deep Convolutional K-Means Clustering [39] presents a framework that integrates convolutional neural network (CNN) architectures with K-means clustering to achieve superior performance in feature extraction while significantly reducing dimensions. This approach is particularly relevant to our research as it similarly bridges supervised and unsupervised learning paradigms. Similarly, other hybrid CNN-K-means approaches [40] showcase how these combined methods can extract optimized features from high-dimensional data while improving energy efficiency in resource-constrained environments. Transformative innovations have also emerged through various approaches that combine deep learning with traditional dimensionality reduction techniques. Research on ClusterGAN [41] demonstrates how Generative Adversarial Networks can be enhanced with clustering capabilities in the latent space, enabling simultaneous generation and clustering of data in a lower-dimensional manifold.