Inicio  /  Sustainability  /  Vol: 11 Núm: 14 Par: July-2 (2019)  /  Artículo
ARTÍCULO
TITULO

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

Resumen

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.

 Artículos similares

       
 
Kaito Furuhashi and Takashi Nakaya    
Global warming is currently progressing worldwide, and it is important to control greenhouse gas emissions from the perspective of adaptation and mitigation. Occupant behavior is highly individualized and must be analyzed to accurately determine a buildi... ver más
Revista: Buildings

 
Ahmad Taha, Basel Barakat, Mohammad M. A. Taha, Mahmoud A. Shawky, Chun Sing Lai, Sajjad Hussain, Muhammad Zainul Abideen and Qammer H. Abbasi    
Accurately looking into the future was a significantly major challenge prior to the era of big data, but with rapid advancements in the Internet of Things (IoT), Artificial Intelligence (AI), and the data availability around us, this has become relativel... ver más
Revista: Future Internet

 
Haijing Huang, Kedi Zhu and Xi Lin    
The full exploration of the energy-saving potential during the operation of buildings is an essential aspect of energy-efficiency retrofitting for existing residential buildings. Setting reasonable energy consumption quotas can promote the improvement of... ver más
Revista: Buildings

 
Pooria Norouzi, Sirine Maalej and Rodrigo Mora    
The development of digital twins leads to the pathway toward intelligent buildings. Today, the overwhelming rate of data in buildings carries a high amount of information that can provide an opportunity for a digital representation of the buildings and e... ver más
Revista: Buildings

 
Yuting Liu, Wenchong Tian, Jun Xie, Weizhong Huang and Kunlun Xin    
With the increasing demands for higher treatment efficiency, better effluent quality, and energy conservation in Urban Wastewater Treatment Plants (WWTPs), research has already been conducted to construct an optimized control system for Anaerobic-Anoxic-... ver más
Revista: Water