## 2 Related Works  
In the domain of supervised classification, various methodologies have been proposed to tackle the challenges posed by batch data and to enhance the interpretability and adaptability of classifiers. Among these, feature interval learning (FIL) techniques have emerged as prominent approaches. FIL approaches offer a distinct paradigm for classification, wherein the feature space is partitioned into intervals for each feature [8]. Each interval is associated with a specific class, enabling a more nuanced representation of feature patterns and enhancing robustness to noise and variability in the data [6]. FIL methods encompass a range of techniques, including hypercuboid or hyperrectangle learning and prototype-based classification, which learn decision boundaries or representative prototypes to distinguish between classes [13]. The concept of hypercuboids has been exploited for many years [14; 15]. Hypercuboid approaches represent one category of FIL methods, where the feature space is partitioned into hypercuboid regions based on the distribution of training data [14]. These regions serve as decision boundaries, facilitating efficient classification of new instances. Hypercuboid approaches are particularly effective in handling high-dimensional data and are known for their computational efficiency and simplicity. Prototype-based FILmethods, on the other hand, generate prototypes for each class based on the distribution of feature intervals in the training data [8; 16]. These prototypes encapsulate the characteristic features of each class and are used to classify new instances by measuring their resemblance to the prototypes. Prototype-based FIL methods offer interpretability and transparency in decision making, as the classification process is based on clear feature ranges. In [6], a family of FIL algorithms is described. Some examples of FIL algorithms are voting feature intervals [8; 13; 17], k-nearest neighbors on feature projections [18], and Naive Bayes classifier (NBC) [1; 19]. The common idea of FIL algorithms is to learn the concept description in the form of a set of disjoint intervals separately for each feature. At the testing phase, the FIL algorithms predict the class of a new data point as the label with the highest total weighted votes of the individual features. For example, in Figure 1, the horizontal axis is feature \(f_{1}\) and the vertical axis is feature \(f_{2}\). The two rectangles represent the two prototypes of classes \(C^{1}\) and \(C^{2}\). The prototype of class \(C^{1}\) is represented by the intervals \(I_{1,1}\) and \(I_{2,1}\) (intervals determined using the training sets of class \(C^{1}\) 'the blue \(x\) sign'). And the prototype of class \(C^{2}\) is represented by the intervals \(I_{1,2}\) and \(I_{2,2}\) (intervals calculated using the training sets of class \(C^{2}\) 'the green \(o\) sign'). The objective is to assign the unlabeled data (the red star \(*\) in Figure 1) to the closest rectangle. Overall, while hypercuboid or hyperrectangle learning algorithms offer computational efficiency and simplicity, FIL methods provide greater flexibility, robustness, and interpretability in classifying new data points, especially in scenarios with complex data distributions and high-dimensional feature spaces [20; 21; 22]. However, while predicting class labels based on the highest total weighted votes of individual features is a straightforward approach in FIL algorithms, it may have limitations in handling feature importance, noise, feature relationships, imbalanced datasets, and the trade-off between interpretability and accuracy. Addressing these inconveniences requires careful consideration of feature selection, noise reduction techniques, feature engineering, and robust mechanisms for handling imbalanced data. While FIL provides a robust foundation for classification, out  
Figure 1: Two-dimensional prototypes of two classes in \(CR\) algorithm. ranking measures offer a complementary approach for handling uncertainty and noise in the data [23].