## 1 Introduction  
One of the fundamental concepts in machine learning is the embedding of data points in an appropriate feature space [1]. The choice of the feature space should be driven by enhancing the topological properties of data clusters, ultimately leading to better performance in pattern recognition tasks such as classification [2, 3, 4], clustering [5, 6, 7], information retrieval [8, 9, 10, 11], and regression [12, 13]. Data points and latent features are two spaces where feature engineering approaches operate. One approach is projecting the data descriptors to an alternative feature space via a hard-coded mathematical function. The evident example of this approach is the use of kernel functions in SVM classifiers, aiming to improve the separability property to allow efficient localization of the hyperplane [14]. Another example is the latent representation yielded on the hidden layers of ANNs [15, 16]. The goal is to increase the compactness of the class samples by abstracting the input data from its specifics and details. Clearly there is a difference between SVMs and ANNs in the intermediate goal; one opts for increasing separability while the other increases compactness. In addition, in the former technique, the mapping is hand-engineered kernels, whereas in the latter the mapping function is learned. On another front, feature transformations retaining the original data space have been limited to feature selection, normalization, and scaling [17]. Conceptually, non-parametric learning approaches such as KNN [18] also belong to this paradigm of feature transformation and can be regarded as a projection of test samples into a subspace of training samples. When the model is presented with unseen data, it uses a distance function to map the data to the most similar pre-seen example. In essence, it is a classification approach with a hypothetical embedded feature transformation step based on a distance mapping function. Limitations of KNN have been thoroughly discussed in the literature [19; 20; 21; 22]. One of its drawbacks is the reliance of the mapping function on a linear distance measure [20] (e.g., Manhattan, Euclidean, Minkowski, and weighted Euclidean distance) between corresponding individual features. However, it neglects the interrelation between features comprising the data samples and their collaborative effect, thus limiting the ability of the distance function in expressing the similarity/dissimilarity of the samples [22]. In addition, the KNN method does not promote properties of compactness and separability of data embeddings in the feature space. Additionally, oversampling approaches operate both spaces [17; 23]. Oversampling creates synthetic examples by relocating feature vectors of given examples along the line segments joining the \(k\) nearest neighbor examples of the same class. Although this approach shows improvement in the accuracy of classification tasks in case of imbalanced and small datasets, it does not improve the compactness and separability of data points. It could happen that a minority class sample could have a majority class sample as its neighbor, which will create false positive samples. State-of-the-art deep learning algorithms have achieved near-human-level accuracy in applications such as computer vision [24; 25; 26; 27; 28]. Nevertheless, one challenge remains for these algorithms when faced with inadequate amounts in acquiring adequate amounts of labeled data [29]. Otherwise, these models are subject to overfitting [30] due to the large number of parameters to learn in complex networks which reduce the network's ability to generalize and cause the network to memorize examples. Also, labeling is widely regarded as a manual labor-intensive and resource-consuming process. In other cases, there are great difficulties in the initial acquisition process of the data samples, a problem that is well-known in the field of bioinformatics [31].