1. Introduction

1.1.         Principles of Classification

Classification is something that exists in nature in a very mysterious way. A class implies the collection of similar observations; the classification problem is the problem when an observation is to be classified in one of the  classes based on the similarity of its characteristics with that of each class. Based on this implication, the characteristics of one or more groups of observations coincide and contrast with other observations. This points to the individuality of that observation and reflects its system profile.

1.2.         The BireyselValue Method

This paper presents a new method for solving a classification problem based on the similarity between the individual traits of a given class and the observation to be classified. In particular, the method process involves three key stages: building, training, and prediction. In the building stage, four steps are involved in creating five parameters. In the training stage, seven steps are involved; consequently,  subsets are formed, where  is equal to the number of classes. Moreover, each observation from the training dataset is transformed into a shape of  and placed into one of the subsets based on its original class. Each subset is considered to represent the individual traits of a given class. Next, the five parameters, the subsets, and a scaled version of the training dataset were saved as a predictive model.
Finally, eleven steps are involved in the prediction stage. The five parameters and the scaled version of the training dataset in the saved predictive model are used to scale and then transform a given observation for which a class is sought. Next, a similarity check between the subsets, which are stored in the saved predictive model as the class individual trait, and the scaled, transformed observation are applied to make the final prediction.
The primary scenario for using the proposed method is as follows: given a training dataset with \(m\)observations, \(n\) variables, and \(k\ge2\) classes, the first two stages involved creating a predictive model. Finally, any given observation with \(n\) variables can be classified using the predictive model after the third stage is applied. Fig. (\ref{931769}) illustrates the workflow of the proposed method.
This paper is organized as follows: section (\ref{507127}) points to the motivation behind the name of the method and the conditions required to implement it. In addition, a training dataset is used as a showcase example to illustrate the steps of implementation. In section (\ref{682342}), the paper introduces in detail the three stages and their steps using a showcase example. Furthermore, the mathematical formulations are discussed. Section (\ref{684945}) outlines the design of the experimental study by describing the experimental methodology and setup, the hyperparameter definitions, the evaluation measures and the results. Section (\ref{533300}) discusses the obtained results from different viewpoints. Finally, Section (\ref{378595}) concludes and presents the main outcomes of the study and some directions for future exploration in the research field.