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K-Nearest Neighbors Under Possibility Framework with Optimizing Parameters

  • Sarra SaiedEmail author
  • Zied Elouedi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)

Abstract

This paper presents a new approach optimizing the parameters in the K-nearest neighbors under possibility framework (KNN-PF) which is based on the classical K-nearest neighbors algorithm within the possibility theory. The KNN-PF method has provided good results comparing to other methods. However, the efficiency of this method is based on its parameters values. Therefore, in order to make this approach more effectiveness, we have to determine the optimal or near-optimal parameters values by maximizing its accuracy classification. In this paper, a novel method was proposed using the powerful evolutionary algorithm Immune Genetic Algorithm (IGA) as an optimizing algorithm. Experiments are conducted based on some real-world datasets and the experimental results show that our proposed method is powerful with respect to the accuracy.

Keywords

Possibility theory KNN under possibility framework (KNN-PF) Optimal parameters Immune Genetic Algorithm (IGA) 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institut Supérieur de Gestion de Tunis, LARODECUniversité de TunisTunisTunisia

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