A WGFS Based Approach to Extract Factors Influencing the Marketing of Korean Language in GCC

Abstract

This research proposed an approach that is intended to determine the minimal set of important factors that influence the desire of learning Korean language in the Gulf Cooperation Council (GCC). Those factors will then influence marketing of the Korean language in GCC by guiding interested people to increase their commercial abilities, improve their information about Korean drama, and prepare them to study or travel to Korea. A total of 500 responses out of 526 questionnaires were used for the analysis process. Merging the weight by SVM and the weight guided feature selection (WGFS) techniques were proposed to build a strong hybrid model of reduction for the investigated dataset. Five different classifiers were used to test the results. Empirical results have showed that the generated factors (the reduct) are very significant to test the ability/inability of learning the Korean language. SVM was shown as the best with accuracy value of 94%. This research contributed to the literature by highlighting the importance of the Korean language in the GCC and by presenting the important factors that influence learners of the Korean language: encouragements and obstacles. Moreover, current research presented the best classifier which yields to the high performance of classification.

Evaluation of COVID-19 Vaccine Refusal among AOU Students in Kuwait and their Families and their Expected Inclination Towards the Acceptance or Refusal of the Vaccine

“…There are many feature selection algorithms used in the field of data science, data mining, machine learning, and others. The reader may refer to [26,27] for some details. The rough set theory (RS) is one of these algorithms.…”