## 1 Introduction  
Classification stands as a cornerstone in machine learning, tasked with assigning new data points to predefined classes. Commonly employed concept models such as Decision Trees (DTs), Support Vector Machines (SVMs), and k-Nearest Neighbors (k-NN) have long served this purpose [1]. Many existing classifiers rely on complex learning mechanisms. These mechanisms combine or transform data features, which can lead to overfitting, high computational costs, and a lack of interpretability, often referred to as the 'black box' effect. Additionally, these models struggle to handle noisy or missing data. To address these limitations, this paper proposes a novel classification framework that leverages feature interval learning (FIL) and outranking measures. FIL partitions the feature space into intervals, associating each interval with a specific class [2, 3]. This approach offers enhanced robustness to noise and variability in the data. Outranking, rooted in preference learning, provides a mechanism for comparing alternatives based on their feature values, further improving the classifier's ability to handle uncertainty and noise [4, 5]. The primary objectives of this study are as follows:  
* Develop classification methods that combine the strengths of FIL and outranking measures to address the shortcomings of existing approaches. * Evaluate the performance of the proposed classifiers on various real-world datasets and compare them to established methods. By achieving these objectives, we aim to contribute to the advancement of classification techniques, offering a more robust, interpretable, and effective solution for a wide range of applications. FIL algorithms segment the feature space into distinct intervals for each feature, with each interval linked to a specific class. Feature intervals represent features as intervals rather than individual values, providing robustness to noise and variability in the input data, particularly for continuous data types [6; 7]. A set of feature intervals embodies a concept in each feature dimension separately. Subsequently, each feature contributes to the classification process by distributing votes among classes, with the predicted class being the one receiving the highest vote [8]. This method's reliance on clear feature ranges offers interpretability, rendering decision rules easy to understand [6]. Furthermore, FIL can be effective with smaller training datasets compared to data-hungry deep learning models [9]. In response to these challenges, we have introduced an approach to supervised classification that harnesses the synergies of feature interval learning and outranking measures, called Closest Resemblance (_CR_). Outranking, entrenched in preference learning [10; 11], transcends simple distance measures by autonomously learning feature intervals from data, thereby capturing nuanced patterns and enhancing adaptation to specific datasets. Moreover, outranking adeptly navigates noise and uncertainties in real-world data through pairwise comparisons on each feature. The proposed _CR_ method amalgamates FIL's interpretability and efficiency with outranking's data-driven pattern recognition and robustness to noise. This integrated approach aims to surmount the limitations of existing methods, particularly in scenarios with imbalanced data and irrelevant features. To validate the effectiveness of our proposed approach, we conducted extensive empirical evaluations on a variety of datasets from the UCI repository [12]. Empirical evaluations compare the proposed classifier _CR_ against established classifiers such as k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), Random Forests (RF), Multi-Layer Perceptron Neural Networks (MLP), Naive Bayes (NB), and Nearest Centroid (NC). The results underscore the efficacy of FIL with outranking, showcasing comparable or superior performance while delivering more interpretable models.