Parameter Optimization of SVM Based on Maximum Variance – Entropy Criterion

2013 ◽  
Vol 373-375 ◽  
pp. 1053-1059
Author(s):  
Jian Liao ◽  
Shao Lei Zhou ◽  
Xian Jun Shi
Keyword(s):  
Parameter Optimization ◽  
Feature Space ◽  
Pso Algorithm ◽  
Parameter Selection ◽  
Support Vector ◽  
Svm Classifier ◽  
Maximum Variance ◽  
Kernel Parameter ◽  
Biquadratic Filter ◽  
Entropy Criterion

Kernel parameter selection of support vector machine (SVM) is difficult in practical application. A parameter selection algorithm of SVM was proposed based on data maximum variance - entropy criterion by analyzing the principle of SVM classifier. The algorithm uses data maximum variance - entropy criterion to measure the linear separability of dataset in the feature space, and combines with particle swarm optimization (PSO) algorithm for parameter optimization. The experiment results on datasets from UCI show that the algorithm is excellence in accuracy and improves the training performance of SVM. To further verify the effectiveness of the algorithm, applying the method in fault diagnosis of biquadratic filter circuit, results prove it improves the diagnostic accuracy.

scholarly journals Parameters Optimization and Application of SVM Based on PCA-Particle Swarm Algorithm

2019 ◽  
pp. 325-330 ◽  
Author(s):  
Qingmi Yang
Keyword(s):  
Parameter Optimization ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Principal Component ◽  
Pso Algorithm ◽  
Parameters Optimization ◽  
Support Vector ◽  
Particle Swarm Algorithm

The parameter optimization of Support Vector Machine (SVM) has been a hot research direction. To improve the optimization rate and classification performance of SVM, the Principal Component Analysis (PCA) - Particle Swarm Optimization (PSO) algorithm was used to optimize the penalty parameters and kernel parameters of SVM. PSO which is to find the optimal solution through continuous iteration combined with PCA that eliminates linear redundancy between data, effectively enhance the generalization ability of the model, reduce the optimization time of parameters, and improve the recognition accuracy. The simulation comparison experiments on 6 UCI datasets illustrate that the excellent performance of the PCA-PSO-SVM model. The results show that the proposed algorithm has higher recognition accuracy and better recognition rate than simple PSO algorithm in the parameter optimization of SVM. It is an effective parameter optimization method.

scholarly journals Staging Melanocytic Skin Neoplasms Using High-Level Pixel-Based Features

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1443
Author(s):  
Mai Ramadan Ibraheem ◽  
Shaker El-Sappagh ◽  
Tamer Abuhmed ◽  
Mohammed Elmogy
Keyword(s):  
Area Under The Curve ◽  
Malignant Neoplasm ◽  
Texture Features ◽  
Feature Space ◽  
Skin Neoplasms ◽  
Support Vector ◽  
Svm Classifier ◽  
Abnormal Growth ◽  
Malignant Skin ◽  
High Level

The formation of malignant neoplasm can be seen as deterioration of a pre-malignant skin neoplasm in its functionality and structure. Distinguishing melanocytic skin neoplasms is a challenging task due to their high visual similarity with different types of lesions and the intra-structural variants of melanocytic neoplasms. Besides, there is a high visual likeliness level between different lesion types with inhomogeneous features and fuzzy boundaries. The abnormal growth of melanocytic neoplasms takes various forms from uniform typical pigment network to irregular atypical shape, which can be described by border irregularity of melanocyte lesion image. This work proposes analytical reasoning for the human-observable phenomenon as a high-level feature to determine the neoplasm growth phase using a novel pixel-based feature space. The pixel-based feature space, which is comprised of high-level features and other color and texture features, are fed into the classifier to classify different melanocyte neoplasm phases. The proposed system was evaluated on the PH2 dermoscopic images benchmark dataset. It achieved an average accuracy of 95.1% using a support vector machine (SVM) classifier with the radial basis function (RBF) kernel. Furthermore, it reached an average Disc similarity coefficient (DSC) of 95.1%, an area under the curve (AUC) of 96.9%, and a sensitivity of 99%. The results of the proposed system outperform the results of other state-of-the-art multiclass techniques.

scholarly journals EXTRACTION OF BUILT-UP AREA BY COMBINING TEXTURAL FEATURES AND SPECTRAL INDICES FROM LANDSAT-8 MULTISPECTRAL IMAGE

2018 ◽  
Vol XLII-5 ◽  
pp. 727-733 ◽  
Author(s):  
V. S. Bramhe ◽  
S. K. Ghosh ◽  
P. K. Garg
Keyword(s):  
Correlation Energy ◽  
Feature Space ◽  
Cost Effective ◽  
Misclassification Error ◽  
Support Vector ◽  
Svm Classifier ◽  
Landsat 8 ◽  
Textural Features ◽  
Spectral Indices ◽  
Spectral Channels

<p><strong>Abstract.</strong> Remote sensing techniques provide efficient and cost-effective approach to monitor the expansion of built-up area, in comparison to other traditional approaches. For extracting built-up class, one of the common approaches is to use spectral and spatial features such as, Normalized Difference Built- up index (NDBI), GLCM texture, Gabor filters etc. However, it is observed that classes such as river soil and fallow land usually mix up with built-up class due to their close spectral similarity. Intermixing of classes have been observed in the classified image when using spectral channels. In this paper, an approach has been proposed which uses urban based spectral indices and textural features to extract built-up areas. Three well known spectral indices i.e. NDBI, Built-up Area Extraction Index (BAEI) and Normalized Difference Bareness Index (NDBai) have been used in this work. Along with spectral indices, local spatial dependency of neighborhood regions is captured using eight GLCM based textural feature, such as, Contrast, Correlation, Energy and Homogeneity etc. for each image band. All textural and spectral indices bands are combined and used for extracting built-up areas using Support Vector Machine (SVM) classifier. Results suggest 4.91% increase in overall accuracy when using texture and spectral indices in comparison with 84.38% overall accuracy achieved when using spectral data only. It is observed that built-up class are more separable in the projected spectral-spatial feature space in comparison to spectral channels. Incorporation of textural features with spectral features reduces the misclassification error and provides results with less salt and pepper noise.</p>

scholarly journals DETERMINATION OF OPTIMUM CLASSIFICATION SYSTEM FOR HYPERSPECTRAL IMAGERY AND LIDAR DATA BASED ON BEES ALGORITHM

2015 ◽  
Vol XL-1-W5 ◽  
pp. 651-656
Author(s):  
F. Samadzadega ◽  
H. Hasani
Keyword(s):  
Urban Area ◽  
Hyperspectral Imagery ◽  
Feature Space ◽  
Classification Performance ◽  
Feature Subset Selection ◽  
Bees Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
Lidar Data ◽  
Feature Subset

Hyperspectral imagery is a rich source of spectral information and plays very important role in discrimination of similar land-cover classes. In the past, several efforts have been investigated for improvement of hyperspectral imagery classification. Recently the interest in the joint use of LiDAR data and hyperspectral imagery has been remarkably increased. Because LiDAR can provide structural information of scene while hyperspectral imagery provide spectral and spatial information. The complementary information of LiDAR and hyperspectral data may greatly improve the classification performance especially in the complex urban area. In this paper feature level fusion of hyperspectral and LiDAR data is proposed where spectral and structural features are extract from both dataset, then hybrid feature space is generated by feature stacking. Support Vector Machine (SVM) classifier is applied on hybrid feature space to classify the urban area. In order to optimize the classification performance, two issues should be considered: SVM parameters values determination and feature subset selection. Bees Algorithm (BA) is powerful meta-heuristic optimization algorithm which is applied to determine the optimum SVM parameters and select the optimum feature subset simultaneously. The obtained results show the proposed method can improve the classification accuracy in addition to reducing significantly the dimension of feature space.

A Wearable Real-time Non-Contact Electrocardiogram System for Arrhythmia Detection and Classification (Preprint)

2020 ◽  
Author(s):  
Hongqiang Li ◽  
Sai Zhang ◽  
Shasha Zuo ◽  
Zhen Zhang ◽  
Binhua Wang ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Real Time ◽  
Predictive Value ◽  
Heart Diseases ◽  
Back Propagation ◽  
Feature Space ◽  
Primary Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
Heart Health

BACKGROUND Driven by the increasing demand for potential patients to monitor their own heart health, wearable technology is increasingly helping people to better monitor their heart health status at a medical level. OBJECTIVE The aim of this study was to develop a flexible and non-contact wearable electrocardiogram system, which can achieve real-time monitoring and primary diagnosis. METHODS A flexible electrocardiogram (ECG) acquisition device (wearable ECG) is designed based on flexible front-end circuit and textile capacitive electrodes, which are based on a conductive textile instead of rigid metal plates. The multi-domain feature space consists of time-domain features and frequency-domain statistical features, which can be used for classification via a back-propagation neural network (BPNN) and a support vector machine (SVM), both of which are optimized using a genetic algorithm. RESULTS The BPNN classifier exhibits good performance, with an accuracy of 98.33%, a sensitivity of 98.33%, a specificity of 99.63% and a positive predictive value of 97.85%. The SVM classifier achieves a higher classification accuracy of 98.89% and also performs better than the BPNN classifier in terms of the sensitivity, specificity and positive predictive value, achieving values of 98.89%, 99.81% and 98.89%, respectively. CONCLUSIONS The experimental results reveal that there is a better classification effect of SVM when classifying normal heart rhythms and 8 types of arrhythmia. The proposed wearable ECG monitoring can aid in the primary diagnosis of certain heart diseases.

Kernel Parameter Selection for SVM Classification

2011 ◽  
pp. 44-55
Author(s):  
Manju Bala ◽  
R. K. Agrawal
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Classification Accuracy ◽  
Parameter Selection ◽  
Kernel Functions ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Svm Classification ◽  
Kernel Parameter ◽  
Benchmark Datasets

The choice of kernel function and its parameter is very important for better performance of support vector machine. In this chapter, the authors proposed few new kernel functions which satisfy the Mercer’s conditions and a robust algorithm to automatically determine the suitable kernel function and its parameters based on AdaBoost to improve the performance of support vector machine. The performance of proposed algorithm is evaluated on several benchmark datasets from UCI repository. The experimental results for different datasets show that the Gaussian kernel is not always the best choice to achieve high generalization of support vector machine classifier. However, with the proper choice of kernel function and its parameters using proposed algorithm, it is possible to achieve maximum classification accuracy for all datasets.

MULTICLASS MOLECULAR CANCER CLASSIFICATION BY KERNEL SUBSPACE METHODS WITH EFFECTIVE KERNEL PARAMETER SELECTION

2005 ◽  
Vol 03 (05) ◽  
pp. 1071-1088 ◽  
Author(s):  
SATOSHI NIIJIMA ◽  
SATORU KUHARA
Keyword(s):  
High Performance ◽  
Molecular Classification ◽  
Parameter Selection ◽  
Cancer Classification ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Classification Problems ◽  
Cancer Treatments ◽  
Kernel Parameter ◽  
Cancer Types

Microarray techniques provide new insights into molecular classification of cancer types, which is critical for cancer treatments and diagnosis. Recently, an increasing number of supervised machine learning methods have been applied to cancer classification problems using gene expression data. Support vector machines (SVMs), in particular, have become one of the most effective and leading methods. However, there exist few studies on the application of other kernel methods in the literature. We apply a kernel subspace (KS) method to multiclass cancer classification problems, and assess its validity by comparing it with multiclass SVMs. Our comparative study using seven multiclass cancer datasets demonstrates that the KS method has high performance that is comparable to multiclass SVMs. Furthermore, we propose an effective criterion for kernel parameter selection, which is shown to be useful for the computation of the KS method.

scholarly journals From pixels to patches: a cloud classification method based on bag of micro-structures

2015 ◽  
Vol 8 (10) ◽  
pp. 10213-10247 ◽  
Author(s):  
Q. Li ◽  
Z. Zhang ◽  
W. Lu ◽  
J. Yang ◽  
Y. Ma ◽  
...  
Keyword(s):  
Image Representation ◽  
Feature Space ◽  
Classification Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Clear Sky ◽  
Cloud Classification ◽  
Image Patches ◽  
Sky Conditions ◽  
Micro Structures

Abstract. Automatic cloud classification has attracted more and more attention with the increasing development of whole sky imagers, but it is still in progress for ground-based cloud observation. This paper proposes a new cloud classification method, named bag of micro-structures (BoMS). This method treats an all-sky image as a collection of micro-structures mapped from image patches, rather than a collection of pixels. And then it constructs an image representation with a weighted histogram of micro-structures. Lastly, a support vector machine (SVM) classifier is applied on the image representation because SVM is appealing for sparse and high dimensional feature space. Five different sky conditions are identified: cirriform, cumuliform, stratiform, clear sky and mixed cloudiness that often appears in all-sky images but is seldom addressed in literature. BoMS is evaluated on a large dataset, which contains 5000 all-sky images that are captured by a total-sky cloud imager located in Tibet (29.25° N, 88.88° E). BoMS achieves an accuracy of 90.9 % for 10 fold cross-validation, and it outperforms the state-of-the-art method with an increase of about 19 %. Furthermore, influence of key parameters in BoMS are investigated to verify their robustness.

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