Feature Selection Method for L1-norm Twin Support Vector Regression

2021 ◽  
Author(s):  
Fan Wang ◽  
Qing Wu ◽  
Yanlin Fu
Keyword(s):  
Feature Selection ◽  
Support Vector Regression ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
L1 Norm ◽  
Twin Support Vector Regression

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).

A fuzzy gaussian rank aggregation ensemble feature selection method for microarray data

2021 ◽  
Vol 24 (4) ◽  
pp. 289-301
Author(s):  
B. Venkatesh ◽  
J. Anuradha
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Performance Metrics ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Selection Methods

In Microarray Data, it is complicated to achieve more classification accuracy due to the presence of high dimensions, irrelevant and noisy data. And also It had more gene expression data and fewer samples. To increase the classification accuracy and the processing speed of the model, an optimal number of features need to extract, this can be achieved by applying the feature selection method. In this paper, we propose a hybrid ensemble feature selection method. The proposed method has two phases, filter and wrapper phase in filter phase ensemble technique is used for aggregating the feature ranks of the Relief, minimum redundancy Maximum Relevance (mRMR), and Feature Correlation (FC) filter feature selection methods. This paper uses the Fuzzy Gaussian membership function ordering for aggregating the ranks. In wrapper phase, Improved Binary Particle Swarm Optimization (IBPSO) is used for selecting the optimal features, and the RBF Kernel-based Support Vector Machine (SVM) classifier is used as an evaluator. The performance of the proposed model are compared with state of art feature selection methods using five benchmark datasets. For evaluation various performance metrics such as Accuracy, Recall, Precision, and F1-Score are used. Furthermore, the experimental results show that the performance of the proposed method outperforms the other feature selection methods.

Feature Selection Method Based on Mutual Information and Support Vector Machine

2021 ◽  
pp. 2150021
Author(s):  
Gang Liu ◽  
Chunlei Yang ◽  
Sen Liu ◽  
Chunbao Xiao ◽  
Bin Song
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Mutual Information ◽  
Classification Accuracy ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Standard Data ◽  
Feature Dimension

A feature selection method based on mutual information and support vector machine (SVM) is proposed in order to eliminate redundant feature and improve classification accuracy. First, local correlation between features and overall correlation is calculated by mutual information. The correlation reflects the information inclusion relationship between features, so the features are evaluated and redundant features are eliminated with analyzing the correlation. Subsequently, the concept of mean impact value (MIV) is defined and the influence degree of input variables on output variables for SVM network based on MIV is calculated. The importance weights of the features described with MIV are sorted by descending order. Finally, the SVM classifier is used to implement feature selection according to the classification accuracy of feature combination which takes MIV order of feature as a reference. The simulation experiments are carried out with three standard data sets of UCI, and the results show that this method can not only effectively reduce the feature dimension and high classification accuracy, but also ensure good robustness.

An Embedded Feature Selection and Multi-Class Classification Method for Detection of the Progression from Mild Cognitive Impairment to Alzheimer's Disease

2020 ◽  
Vol 10 (2) ◽  
pp. 370-379 ◽  
Author(s):  
Jie Cai ◽  
Lingjing Hu ◽  
Zhou Liu ◽  
Ke Zhou ◽  
Huailing Zhang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Treatment Plan ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Feature Subset

Background: Mild cognitive impairment (MCI) patients are a high-risk group for Alzheimer's disease (AD). Each year, the diagnosed of 10–15% of MCI patients are converted to AD (MCI converters, MCI_C), while some MCI patients remain relatively stable, and unconverted (MCI stable, MCI_S). MCI patients are considered the most suitable population for early intervention treatment for dementia, and magnetic resonance imaging (MRI) is clinically the most recommended means of imaging examination. Therefore, using MRI image features to reliably predict the conversion from MCI to AD can help physicians carry out an effective treatment plan for patients in advance so to prevent or slow down the development of dementia. Methods: We proposed an embedded feature selection method based on the least squares loss function and within-class scatter to select the optimal feature subset. The optimal subsets of features were used for binary classification (AD, MCI_C, MCI_S, normal control (NC) in pairs) based on a support vector machine (SVM), and the optimal 3-class features were used for 3-class classification (AD, MCI_C, MCI_S, NC in triples) based on one-versus-one SVMs (OVOSVMs). To ensure the insensitivity of the results to the random train/test division, a 10-fold cross-validation has been repeated for each classification. Results: Using our method for feature selection, only 7 features were selected from the original 90 features. With using the optimal subset in the SVM, we classified MCI_C from MCI_S with an accuracy, sensitivity, and specificity of 71.17%, 68.33% and 73.97%, respectively. In comparison, in the 3-class classification (AD vs. MCI_C vs. MCI_S) with OVOSVMs, our method selected 24 features, and the classification accuracy was 81.9%. The feature selection results were verified to be identical to the conclusions of the clinical diagnosis. Our feature selection method achieved the best performance, comparing with the existing methods using lasso and fused lasso for feature selection. Conclusion: The results of this study demonstrate the potential of the proposed approach for predicting the conversion from MCI to AD by identifying the affected brain regions undergoing this conversion.

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