Evaluating the Effect of Optimized Voting Using Hybrid Particle Swarm and Grey Wolf Algorithm on the Classification of the Zoo Dataset

Doaa S. Khafaga^*1, Hussein Alkattan², Alhumaima A. Subhi³

¹ Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

²Department of System Programming South Ural State University; pr. Lenina 76, Chelyabinsk, 454080 Russia

³Electronic and Computer Center, University of Diyala, Baqubah MJJ2+R9G, Iraq

           Emails:dskhafga@pnu.edu.sa; alkattan.hussein92@gmail.com, alhumaimaali@uodiyala.edu.iq

Abstract

When there are numerous possible solutions for a given class in a given problem, majority voting or plurality voting is typically employed. One common technique for improving classification accuracy is bagging, which involves training many classifiers on slightly different datasets and then voting on the combined results. In this research, we examine how alternative voting procedures affect the efficiency of two distinct classification algorithms applied to datasets of varying complexity. Despite the increased computing cost associated with determining preference order, the results show that the single transferable vote can be a suitable alternative to plurality voting.

Keywords: Zoo data; Voting classifier; Support vector manchines; Neural networks; Decision trees.