Neutrosophic Weighted Support Vector Machines for the Determination of School Administrators Who Attended an Action Learning Course Based on Their Conflict-Handling Styles

Muhammed Turhan,Florentin Smarandache,Dönüş Şengür,Songül Karabatak,Yanhui Guo

doi:10.3390/sym10050176

Muhammed Turhan, Florentin Smarandache + Show 3 more

Open Access

https://doi.org/10.3390/sym10050176

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Abstract

In the recent years, school administrators often come across various problems while teaching, counseling, and promoting and providing other services which engender disagreements and interpersonal conflicts between students, the administrative staff, and others. Action learning is an effective way to train school administrators in order to improve their conflict-handling styles. In this paper, a novel approach is used to determine the effectiveness of training in school administrators who attended an action learning course based on their conflict-handling styles. To this end, a Rahim Organization Conflict Inventory II (ROCI-II) instrument is used that consists of both the demographic information and the conflict-handling styles of the school administrators. The proposed method uses the Neutrosophic Set (NS) and Support Vector Machines (SVMs) to construct an efficient classification scheme neutrosophic support vector machine (NS-SVM). The neutrosophic c-means (NCM) clustering algorithm is used to determine the neutrosophic memberships and then a weighting parameter is calculated from the neutrosophic memberships. The calculated weight value is then used in SVM as handled in the Fuzzy SVM (FSVM) approach. Various experimental works are carried in a computer environment out to validate the proposed idea. All experimental works are simulated in a MATLAB environment with a five-fold cross-validation technique. The classification performance is measured by accuracy criteria. The prediction experiments are conducted based on two scenarios. In the first one, all statements are used to predict if a school administrator is trained or not after attending an action learning program. In the second scenario, five independent dimensions are used individually to predict if a school administrator is trained or not after attending an action learning program. According to the obtained results, the proposed NS-SVM outperforms for all experimental works.

Highlights

Support Vector Machine (SVM) is a widely used supervised classifier, which has provided better achievements than traditional classifiers in many pattern recognition applications in the last two decades [1]
SVM approaches and Fuzzy SVM (FSVM) are used in performance comparison
The experimental works are carried out with a five-fold cross-validation technique and the classification accuracy is measured to evaluate the performance of the proposed Neutrosophic Set (NS)-SVM approach

Summary

Introduction

Support Vector Machine (SVM) is a widely used supervised classifier, which has provided better achievements than traditional classifiers in many pattern recognition applications in the last two decades [1]. This misclassification may arise due to noises or other conditions To handle such a problem, Lin et al proposed Fuzzy SVMs (FSVMs), in which a fuzzy membership is assigned to each input data point [3]. A robust SVM architecture is constructed by combining the fuzzy memberships into the learning of the decision surface Another fuzzy-based improved SVMs approach was proposed by Wang et al The authors applied it to a credit risk analysis of consumer lending [4]. Action learning is an effective way to train school administrators in order to improve their conflict-handling styles To this end, the developed NS-SVM approach is applied to determine the effectiveness of training in school administrators who attended an action learning course based on their conflict-handling styles.

Related Works

Neutrosophic c-Means Clustering

Experimental Work and Results

Conclusions