Using AHP Method for Educational and Vocational Guidance

Abstract

This paper presents a decision support tool for educational and vocational guidance, based on the supervised classification method k-nearest neighbors (KNN). This method consists in determining, for each new observation to be classified, the list of nearest neighbors of the observations already classified. The use of the KNN method requires choosing a distance and the most classical one is the Euclidean distance. In the context of this work, two functions were tested to measure resemblance as far as similarity and dissimilarity are concerned.