This paper presents a new method for solving a classification problem; the  BireyselValue method assumes that the individual traits of a class help to classify an observation based on similarity measures. The method involves three stages to solve the classification problem:  the building stage, the training stage, and the prediction stage. The first two stages accomplish two key steps: firstly, five parameters are used to transform any observation of size $n$n​ variables into six variables; secondly, subsets of the individual traits of each class are created. As a result, the parameters, the subsets of the individual traits, and a scaled version of the training dataset are saved as a predictive model. Ultimately, the prediction stage uses the elements in the predictive model to transform the observations that are to be classified and of size $n$n​ into the size of six variables and to perform similarity measures between the observation and the individual traits of class to make the final prediction. The experimental results obtained on 6 multiclass datasets from different domains showed that the proposed method is efficient at solving classification problems. Moreover, the method can potentially be used for purposes other than solving a classification problem.

Keywords: BireyselValue Method, Classification, Prediction, Dimension Reduction