Prediction of Consumption Choices of Low-Income Groups in a Mixed-Income Community Using a Support Vector Machine Method

Abstract

To examine how cross-strata neighboring behavior in a mixed-income community can influence the consumption choices of individuals in low-income groups, and to improve the prediction accuracy of the consumption choice model of low-income groups for small sample sizes, we developed a support vector machine (SVM) algorithm based on the influence of neighboring behavior. We substituted the predicted latent variables into the SVM classifier and constructed an SVM prediction model with latent variables based on reference group theory. We established the model parameters using cross-validation and used low-income residents from a mixed-income community in Shanghai as study objects to empirically test the model’s performance. The results show that the SVM selection model with latent variables has good prediction accuracy. The proposed model’s accuracy was improved by 1.29% on the basis of the particle swarm optimization (PSO)-SVM model without latent variables, and by 19.35% on the basis of the SVM model with latent variables. The proposed model can be employed to predict the consumption choices of individuals in low-income groups. This paper offers a theoretical reference for investigating neighboring behavior in a mixed-income community and the consumption choices of individuals in low-income groups and is practically important for urban community planning systems.

Integrating Qualitative Comparative Analysis and Support Vector Machine Methods to Reduce Passengers’ Resistance to Biometric E-Gates for Sustainable Airport Operations

“…Zu et al [8] Optimal Precision Alimi et al [9] Linear, Poly, RBF Precision, Recall, F-Score Xue et al [10] RBF Accuracy Olivares-Mercado et al [12] RBF Precision, Recall, Accuracy, F-Score Joshi et al [59] RBF Accuracy Ahmad et al [16] RBF Accuracy Aruna et al [60] RBF Accuracy Abdelaal et al [15] RBF AUC You and Rumbe [20] Poly, RBF, Sigmoid Accuracy Huang et al [17] RBF Accuracy…”

“…The SVM methodology, which belongs to intellectual machine-learning algorithms, has been actively used within the field of sustainability research [8][9][10][11][12]. Among the machine-learning algorithms, such as linear discriminate analysis, decision trees, logistic regression, naïve Bayes, artificial neural networks and k-nearest neighbor, SVM is a tried and tested algorithm that has gained much trust amongst academics [13,14].…”