Robust and SparseLP-Norm Support Vector Regression

2017 ◽  
Vol 21 (6) ◽  
pp. 989-997
Author(s):  
Ya-Fen Ye ◽  
Chao Ying ◽  
Yuan-Hai Shao ◽  
Chun-Na Li ◽  
Yu-Juan Chen ◽  
...  
Keyword(s):  
Feature Selection ◽  
Theoretical Analysis ◽  
Support Vector Regression ◽  
Experimental Results ◽  
Support Vector ◽  
The Absolute

A robust and sparseLp-norm support vector regression (Lp-RSVR) is proposed in this paper. The implementation of feature selection in ourLp-RSVR not only preserves the performance of regression but also improves its robustness. The main characteristics ofLp-RSVR are as follows: (i) By using the absolute constraint,Lp-RSVR performs robustly against outliers. (ii)Lp-RSVR ensures that useful features are selected based on theoretical analysis. (iii) Based on the feature-selection results, nonlinearLp-RSVR can be used when data is structurally nonlinear. Experimental results demonstrate the superiorities of the proposedLp-RSVR in both feature selection and regression performance as well as its robustness.

WaveletLp-Norm Support Vector Regression with Feature Selection

2015 ◽  
Vol 19 (3) ◽  
pp. 407-416 ◽  
Author(s):  
Ya-Fen Ye ◽  
◽  
Yuan-Hai Shao ◽  
Chun-Na Li ◽  
Keyword(s):  
Feature Selection ◽  
Real Estate ◽  
Theoretical Analysis ◽  
Support Vector Regression ◽  
Housing Prices ◽  
Support Vector ◽  
Powerful Factor ◽  
Regression Problems ◽  
Real Estate Prices ◽  
Performance Results

This paper proposes waveletLp-norm support vector regression (Lp-WSVR) to solve feature selection and regression problems effectively. Unlike conventional support vector regression (SVR), linearLp-WSVR ensures that useful features are selected based on theoretical analysis. By using the wavelet kernel,Lp-WSVR approaches any curve in quadratic continuous integral space that leads to improving regression performance. Results of experiments show the superiority ofLp-WSVR in both feature selection and regression performances. ApplyingLp-WSVR to Chinese real estate prices shows that the most significant and powerful factor contributing to Chinese housing prices is monetary growth.

Forecasting Demand With Support Vector Regression Technique Incorporating Feature Selection in the Presence of Calendar Effect

2018 ◽  
pp. 302-316 ◽  
Author(s):  
Malek Sarhani ◽  
Abdellatif El Afia
Keyword(s):  
Time Series ◽  
Feature Selection ◽  
Supply Chains ◽  
Support Vector Regression ◽  
Case Studies ◽  
Time Series Forecasting ◽  
Regression Technique ◽  
Support Vector ◽  
Reliable Prediction ◽  
Forecasting Methods

Reliable prediction of future demand is needed to better manage and optimize supply chains. However, a difficulty of forecasting demand arises due to the fact that heterogeneous factors may affect it. Analyzing such data by using classical time series forecasting methods will fail to capture such dependency of factors. This chapter addresses these problems by examining the use of feature selection in forecasting using support vector regression while eliminating the calendar effect using X13-ARIMA-SEATS. The approach is investigated in three different case studies.

Computer-Aided Detection of Polyps in CT Colonography by Means of Feature Selection and Massive-Training Support Vector Regression

2012 ◽  
pp. 178-201
Author(s):  
Jian-Wu Xu ◽  
Kenji Suzuki
Keyword(s):  
Feature Selection ◽  
Support Vector Regression ◽  
Ct Colonography ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Computer Aided Detection ◽  
Training Time ◽  
Computer Aided ◽  
Wilks Lambda

One of the major challenges in current Computer-Aided Detection (CADe) of polyps in CT Colonography (CTC) is to improve the specificity without sacrificing the sensitivity. If a large number of False Positive (FP) detections of polyps are produced by the scheme, radiologists might lose their confidence in the use of CADe. In this chapter, the authors used a nonlinear regression model operating on image voxels and a nonlinear classification model with extracted image features based on Support Vector Machines (SVMs). They investigated the feasibility of a Support Vector Regression (SVR) in the massive-training framework, and the authors developed a Massive-Training SVR (MTSVR) in order to reduce the long training time associated with the Massive-Training Artificial Neural Network (MTANN) for reduction of FPs in CADe of polyps in CTC. In addition, the authors proposed a feature selection method directly coupled with an SVM classifier to maximize the CADe system performance. They compared the proposed feature selection method with the conventional stepwise feature selection based on Wilks’ lambda with a linear discriminant analysis classifier. The FP reduction system based on the proposed feature selection method was able to achieve a 96.0% by-polyp sensitivity with an FP rate of 4.1 per patient. The performance is better than that of the stepwise feature selection based on Wilks’ lambda (which yielded the same sensitivity with 18.0 FPs/patient). To test the performance of the proposed MTSVR, the authors compared it with the original MTANN in the distinction between actual polyps and various types of FPs in terms of the training time reduction and FP reduction performance. The authors’ CTC database consisted of 240 CTC datasets obtained from 120 patients in the supine and prone positions. With MTSVR, they reduced the training time by a factor of 190, while achieving a performance (by-polyp sensitivity of 94.7% with 2.5 FPs/patient) comparable to that of the original MTANN (which has the same sensitivity with 2.6 FPs/patient).

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