SOM-Optimized Neurofuzzy Classifiers for Measuring Expatriation Willingness

Abstract

The authors developed a self-organizing map-based optimization (SOMO) neurofuzzy classifier to deal with a practical expatriation willingness (EW) problem, which is associated with employees' willingness to accept expatriate assignments. The proposed model also delivers more information about rule coverage and generates user-friendly outcomes. The authors adopt the SOMO algorithm to optimize the weights of neurons in the proposed hybrid neurofuzzy classifier. They evaluated the model's feasibility using the databases from previous studies exploring employees' EW. The results show that the proposed neurofuzzy classifier yields 11 determination rules whose accuracy rates are greater than 80 percent. The contribution to the body of knowledge lies in the significant improvement in accuracy rates and coverage for predicting EW rules, optimization for all the network parameters using a novel algorithm, and more friendly outputs for practical use.