## 2 Related Works
Beyond traditional majority voting approaches, another advancement in FIL is the integration of outranking measures into the classification process [24]. Outranking measures, rooted in preference learning principles, extend the classification paradigm by capturing subtle preferences and trade-offs among alternative solutions [25]. By conducting pairwise comparisons on each feature, outranking measures facilitate more nuanced decision making and enhance adaptability to specific datasets [10; 23]. Several FIL classifiers based on outranking measures have been developed through the decades, among them are PROAFTN [16], and K Closest Resemblance (K-CR) [26; 27]. These classifiers offer several advantages in the context of classification tasks. By evaluating the data points based on their pairwise performance on each feature, outranking techniques can capture subtle differences in feature importance and contribution to class membership. Instead of blindly aggregating votes from all features, outranking considers the relative performance of alternatives on each feature, effectively filtering out noise and focusing on discriminative features that contribute most to the classification decision. This robustness to noise enhances the reliability and accuracy of classification outcomes. Outranking techniques can capture complex relationships between features by conducting pairwise comparisons on each feature individually. Unlike majority voting, which treats features independently, outranking takes into account the interactions and dependencies between features, allowing for more accurate and nuanced classification decisions [28]. Outranking techniques provide clear and interpretable decision rules by quantifying the resemblance relationship between data points. Instead of relying on a simple count of votes, outranking assigns a degree of resemblance between alternatives based on their pairwise comparisons, making the classification process more transparent and understandable [16]. This enhanced interpretability fosters trust and understanding in the classification model's decision-making process. Outranking techniques can effectively handle imbalanced datasets by considering the relative performance of alternatives on each feature [29]. Instead of biasing the classification decision towards the majority class, outranking evaluates alternatives based on their overall resemblance relationship, ensuring that all classes are appropriately considered in the classification process. This helps mitigate the impact of class imbalance and ensures more balanced and accurate predictions. Outranking techniques offer flexibility and adaptability to different types of data and classification tasks [28]. By capturing subtle differences in feature importance and performance, outranking can accommodate various data distributions and class structures, making it suitable for a wide range of classification scenarios. The FIL classifiers based on outranking measures have been applied to the resolution of many real world practical problems such as medical diagnosis [30; 31], image processing and classification [29; 32], engineering design [33], recommendation system [27], cybersecurity [34], and inventory management [35]. Even though these classifiers have several advantages, they suffer from computational complexity in their learning phase, specifically when the number of features is very large. The PROAFTN and the K-CR build a set of prototypes in each class and then the inductive is based on the recursive approach, which is very complex. In this paper, we introduce an FIL approach based on outranking measures for classification problems called Closest Resemblance, _CR_. The _CR_ algorithm is simple, effective, efficient, and robust to irrelevant features. In the remaining paper, we present the _CR_ classifier, where each class is represented by one prototype and the classification rule is expressed as \"the data point is assigned to the class if the data point is resembled or roughly equivalent to the prototype of this class\".