scholarly journals A Wavelet Kernel-Based Primal Twin Support Vector Machine for Economic Development Prediction

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Fang Su ◽  
HaiYang Shang
Keyword(s):  
Support Vector Machine ◽  
Economic Development ◽  
Prediction Method ◽  
Twin Support Vector Machine ◽  
Development Economic ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Machine Model ◽  
Important Indicator ◽  
Quadratic Programming Problems

Economic development forecasting allows planners to choose the right strategies for the future. This study is to propose economic development prediction method based on the wavelet kernel-based primal twin support vector machine algorithm. As gross domestic product (GDP) is an important indicator to measure economic development, economic development prediction means GDP prediction in this study. The wavelet kernel-based primal twin support vector machine algorithm can solve two smaller sized quadratic programming problems instead of solving a large one as in the traditional support vector machine algorithm. Economic development data of Anhui province from 1992 to 2009 are used to study the prediction performance of the wavelet kernel-based primal twin support vector machine algorithm. The comparison of mean error of economic development prediction between wavelet kernel-based primal twin support vector machine and traditional support vector machine models trained by the training samples with the 3–5 dimensional input vectors, respectively, is given in this paper. The testing results show that the economic development prediction accuracy of the wavelet kernel-based primal twin support vector machine model is better than that of traditional support vector machine.

A New Fuzzy Support Vector Machine for Credit Scoring

2011 ◽  
Vol 109 ◽  
pp. 636-640
Author(s):  
Bo Tang ◽  
Min Xia
Keyword(s):  
Support Vector Machine ◽  
Economic Development ◽  
Prediction Accuracy ◽  
Credit Scoring ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Good Prediction ◽  
Support Vector Machine Algorithm ◽  
Fuzzy Support Vector Machine ◽  
Good Prediction Accuracy

With China's rapid economic development, credit scoring has become very important. This paper presents a new fuzzy support vector machine algorithm used to solve the problems of credit scoring. The empirical results show that the proposed fuzzy membership model is valid ,the algorithm has good prediction accuracy and anti-noise ability.

CPI Big Data Prediction Based on Wavelet Twin Support Vector Machine

2020 ◽  
pp. 2159013
Author(s):  
Yiqing Fan ◽  
Zhihui Sun
Keyword(s):  
Support Vector Machine ◽  
Big Data ◽  
Consumer Price Index ◽  
Low Frequency ◽  
Prediction Method ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Reconstruction Method ◽  
Data Prediction ◽  
Svm Model

In order to effectively improve the accuracy of Consumer Price Index (CPI) prediction so as to more truly reflect the overall level of the country’s macroeconomic situation, a CPI big data prediction method based on wavelet twin support vector machine (SVM) is proposed. First, the historical CPI data are decomposed into high-frequency part and low-frequency part by wavelet transform. Then a more advanced twin SVM is used to build a prediction model to obtain two kinds of prediction results. Finally, the wavelet reconstruction method is used to fuse the two kinds of prediction results to obtain the final CPI prediction results. The wavelet twin SVM model is used to fit and predict CPI index. Experimental results show that compared with the similar prediction methods, the proposed prediction method has higher fitting accuracy and smaller root mean square error.

A Novel Twin Support Vector Machines with Manifold Regularization

2014 ◽  
Vol 644-650 ◽  
pp. 1640-1643
Author(s):  
Xiao Peng Hua ◽  
Xian Feng Li
Keyword(s):  
Support Vector Machine ◽  
Classification Performance ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Local Geometry ◽  
Manifold Regularization ◽  
Scatter Matrix ◽  
Geometry Structure ◽  
Proximal Support Vector Machine ◽  
Quadratic Programming Problems

Twin support vector machine (TWSVM), as a variant of the generalized eigenvalue proximal support vector machine (GEPSVM), attempts to improve the generalization of GEPSVM, whose solution follows from solving two quadratic programming problems (QPPs), each of which is smaller than in a standard SVM. Unfortunately, TWSVM fails to fully consider the local geometry structure and the local underlying descriminant information inside the samples that may be important for classification performance and only preserves the global data structure. In this paper, a novel TWSVM with manifold regularization is proposed by introducing the basic idea of the locality preserving within-class scatter matrix (LPWSM) into TWSVM. We termed this method manifold TWSVM (MTWSVM). MTWSVM not only retains the superior characteristics of TWSVM, but also preserves the local geometry structure between samples and shows the local underlying discriminant information. Experimental results confirm the effectiveness of our method.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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