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

2019 ◽  
Vol 11 (14) ◽  
pp. 3981 ◽  
Author(s):  
Xiaoqian Zu ◽  
Yongxiang Wu ◽  
Zhenduo Zhang ◽  
Lu Yu
Keyword(s):  
Support Vector Machine ◽  
Low Income ◽  
Latent Variables ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Income Groups ◽  
Consumption Choices ◽  
Svm Model ◽  
Low Income Groups ◽  
Mixed Income

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.

scholarly journals Stable Isotope Ratio and Elemental Profile Combined with Support Vector Machine for Provenance Discrimination of Oolong Tea (Wuyi-Rock Tea)

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yun-xiao Lou ◽  
Xian-shu Fu ◽  
Xiao-ping Yu ◽  
Zi-hong Ye ◽  
Hai-feng Cui ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Stable Isotope ◽  
Isotope Ratio ◽  
Prediction Accuracy ◽  
Stable Isotope Ratio ◽  
Classification Model ◽  
Support Vector ◽  
Svm Model ◽  
Metallic Element ◽  
Instrumental Methods

This paper focused on an effective method to discriminate the geographical origin of Wuyi-Rock tea by the stable isotope ratio (SIR) and metallic element profiling (MEP) combined with support vector machine (SVM) analysis. Wuyi-Rock tea (n=99) collected from nine producing areas and non-Wuyi-Rock tea (n=33) from eleven nonproducing areas were analysed for SIR and MEP by established methods. The SVM model based on coupled data produced the best prediction accuracy (0.9773). This prediction shows that instrumental methods combined with a classification model can provide an effective and stable tool for provenance discrimination. Moreover, every feature variable in stable isotope and metallic element data was ranked by its contribution to the model. The results show that δ2H, δ18O, Cs, Cu, Ca, and Rb contents are significant indications for provenance discrimination and not all of the metallic elements improve the prediction accuracy of the SVM model.

scholarly journals Short-term Load Forecasting Model for Microgrid Based on HSA-SVM

2018 ◽  
Vol 173 ◽  
pp. 01007
Author(s):  
Han Aoyang ◽  
Yu Litao ◽  
An Shuhuai ◽  
Zhang Zhisheng
Keyword(s):  
Support Vector Machine ◽  
Prediction Accuracy ◽  
Harmony Search ◽  
Load Forecasting ◽  
Support Vector ◽  
Forecasting Model ◽  
Short Term ◽  
Search Optimization ◽  
Svm Model ◽  
Short Term Load Forecasting

Short-term load forecasting for microgrid is the basis of the research on scheduling techniques of microgrid. Accurate load forecasting for microgrid will provide the necessary basis for cooperative optimization scheduling. Short-term loadforecasting model for microgrid based on support vector machine(SVM) is constructed in this paper. The harmony search optimization algorithm(HSA) is used to optimize the parameters of the SVM model, because it has the advantages of fast convergence speed and better optimization ability. Through the simulation and test of the actual microgrid load system, it is proved that the short-term loadforecasting model for microgrid based on HSA-SVM can effectively improve the prediction accuracy.

scholarly journals Cyberbullying Sentiment Analysis with Word2Vec and One-Against-All Support Vector Machine

2021 ◽  
Vol 8 (1) ◽  
pp. 57-64
Author(s):  
Lionel Reinhart Halim ◽  
Alethea Suryadibrata
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Prediction Accuracy ◽  
Word Embedding ◽  
Support Vector ◽  
Svm Model ◽  
Index Terms ◽  
Tuning Process ◽  
Negative Impacts ◽  
Randomized Search

Depression and social anxiety are the two main negative impacts of cyberbullying. Unfortunately, a survey conducted by UNICEF on 3rd September 2019 showed that 1 in 3 young people in 30 countries had been victims of cyberbullying. Sentiment analysis research will be conducted to detect a comment that contains cyberbullying. Dataset of cyberbullying is obtained from the Kaggle website, named, Toxic Comment Classification Challenge. The pre-processing process consists of 4 stages, namely comment generalization (convert text into lowercase and remove punctuation), tokenization, stop words removal, and lemmatization. Word Embedding will be used to conduct sentiment analysis by implementing Word2Vec. After that, One-Against-All (OAA) method with the Support Vector Machine (SVM) model will be used to make predictions in the form of multi labelling. The SVM model will go through a hyperparameter tuning process using Randomized Search CV. Then, evaluation will be carried out using Micro Averaged F1 Score to assess the prediction accuracy and Hamming Loss to assess the numbers of pairs of sample and label that are incorrectly classified. Implementation result of Word2Vec and OAA SVM model provide the best result for the data undergoing the process of pre-processing using comment generalization, tokenization, stop words removal, and lemmatization which is stored into 100 features in Word2Vec model. Micro Averaged F1 and Hamming Loss percentage that is produced by the tuned model is 83.40% and 15.13% respectively.   Index Terms— Sentiment Analysis; Word Embedding; Word2Vec; One-Against-All; Support Vector Machine; Toxic Comment Classification Challenge; Multi Labelling

Planetary gentrification and urban (re)development

2019 ◽  
Vol 61 (1) ◽  
pp. 5-13 ◽  
Author(s):  
Loretta Lees
Keyword(s):  
Low Income ◽  
Policy Making ◽  
Large Scale ◽  
Urban Redevelopment ◽  
Urban Transformation ◽  
Small Scale ◽  
Income Groups ◽  
Low Income Groups

Abstract Gentrification is no-longer, if it ever was, a small scale process of urban transformation. Gentrification globally is more often practised as large scale urban redevelopment. It is state-led or state-induced. The results are clear – the displacement and disenfranchisement of low income groups in favour of wealthier in-movers. So, why has gentrification come to dominate policy making worldwide and what can be done about it?

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

scholarly journals Three-Dimensional Site Characterization Model of Bangalore Using Support Vector Machine

2012 ◽  
Vol 2012 ◽  
pp. 1-10
Author(s):  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
Three Dimensional ◽  
Site Characterization ◽  
Standard Penetration Test ◽  
Support Vector ◽  
Characterization Model ◽  
Svm Model ◽  
Input Variables ◽  
Learning Machine ◽  
A Site

The main objective of site characterization is the prediction of in situ soil properties at any half-space point at a site based on limited tests. In this study, the Support Vector Machine (SVM) has been used to develop a three dimensional site characterization model for Bangalore, India based on large amount of Standard Penetration Test. SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing ε-insensitive loss function. The database consists of 766 boreholes, with more than 2700 field SPT values () spread over 220 sq km area of Bangalore. The model is applied for corrected () values. The three input variables (, , and , where , , and are the coordinates of the Bangalore) were used for the SVM model. The output of SVM was the data. The results presented in this paper clearly highlight that the SVM is a robust tool for site characterization. In this study, a sensitivity analysis of SVM parameters (σ, , and ε) has been also presented.

New Trends in Interaction of Developed and Developing Countries in Innovation Sphere

2012 ◽  
pp. 3-14
Author(s):  
A. Eroshkin ◽  
M. Petrov
Keyword(s):  
Developing Countries ◽  
International Relations ◽  
Low Income ◽  
Large Scale ◽  
Research Capacity ◽  
Transnational Corporations ◽  
Income Groups ◽  
Global Innovation ◽  
Low Income Groups ◽  
Adaptive Nature

The economic and innovative rise of the developing states stimulated a deep restructuring of the existing system of international relations in science and technology sphere. As the article points, one of the main manifestations of this trend can be seen in the transformation of global innovation strategies of transnational corporations. The world’s largest TNCs, mostly based in the industrial nations, have begun to transfer growing segments and parts of their R&D programs to the developing countries in order to take advantage of their increased research capacity. As a result, the nature of the projects being implemented there by the TNCs is changing. Historically, the TNCs’ local R&D activities were of adaptive nature. Namely, the stress was made on modification of the products and services offered by the TNCS globally to the specifics of local markets. Currently, a growing number of transnational corporations are implementing the large-scale programs in the developing countries aimed at designing new types of products, including those targeted at the low-income groups of consumers that make up the bulk of the population in developing countries.

Sign in / Sign up

Export Citation Format

Share Document