scholarly journals An Improved Skewness Decision Tree SVM Algorithm for the Classification of Steel Cord Conveyor Belt Defects

2018 ◽  
Vol 8 (12) ◽  
pp. 2574 ◽  
Author(s):  
Qinghua Mao ◽  
Hongwei Ma ◽  
Xuhui Zhang ◽  
Guangming Zhang
Keyword(s):  
Decision Tree ◽  
Classification Accuracy ◽  
Pso Algorithm ◽  
Conveyor Belt ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Data Sets ◽  
Classification Problems ◽  
Steel Cord

Skewness Decision Tree Support Vector Machine (SDTSVM) algorithm is widely known as a supervised learning model for multi-class classification problems. However, the classification accuracy of the SDTSVM algorithm depends on the perfect selection of its parameters and the classification order. Therefore, an improved SDTSVM (ISDTSVM) algorithm is proposed in order to improve the classification accuracy of steel cord conveyor belt defects. In the proposed model, the classification order is determined by the sum of the Euclidean distances between multi-class sample centers and the parameters are optimized by the inertia weight Particle Swarm Optimization (PSO) algorithm. In order to verify the effectiveness of the ISDTSVM algorithm with different feature space, experiments were conducted on multiple UCI (University of California Irvine) data sets and steel cord conveyor belt defects using the proposed ISDTSVM algorithm and the conventional SDTSVM algorithm respectively. The average classification accuracies of five-fold cross-validation were obtained, based on two kinds of kernel functions respectively. For the Vowel, Zoo, and Wine data sets of the UCI data sets, as well as the steel cord conveyor belt defects, the ISDTSVM algorithm improved the classification accuracy by 3%, 3%, 1% and 4% respectively, compared to the SDTSVM algorithm. The classification accuracy of the radial basis function kernel were higher than the polynomial kernel. The results indicated that the proposed ISDTSVM algorithm improved the classification accuracy significantly, compared to the conventional SDTSVM algorithm.

scholarly journals Feature Extraction and Classification of MRI Using Hybrid RBF Kernel and SVM

2021 ◽  
pp. 418-426
Author(s):  
Suhas S ◽  
Dr. C. R. Venugopal
Keyword(s):  
Support Vector Machine ◽  
Image Retrieval ◽  
Classification Accuracy ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Mr Images ◽  
Rbf Kernel

An enhanced classification system for classification of MR images using association of kernels with support vector machine is developed and presented in this paper along with the design and development of content-based image retrieval (CBIR) system. Content of image retrieval is the process of finding relevant image from large collection of image database using visual queries. Medical images have led to growth in large image collection. Oriented Rician Noise Reduction Anisotropic Diffusion filter is used for image denoising. A modified hybrid Otsu algorithm termed is used for image segmentation. The texture features are extracted using GLCM method. Genetic algorithm with Joint entropy is adopted for feature selection. The classification is done by support vector machine along with various kernels and the performance is validated. A classification accuracy of 98.83% is obtained using SVM with GRBF kernel. Various features have been extracted and these features are used to classify MR images into five different categories. Performance of the MC-SVM classifier is compared with different kernel functions. From the analysis and performance measures like classification accuracy, it is inferred that the brain and spinal cord MRI classification is best done using MC- SVM with Gaussian RBF kernel function than linear and polynomial kernel functions. The proposed system can provide best classification performance with high accuracy and low error rate.

scholarly journals Kernel Based Data-Adaptive Support Vector Machines for Multi-Class Classification

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 936
Author(s):  
Jianli Shao ◽  
Xin Liu ◽  
Wenqing He
Keyword(s):  
Machine Learning ◽  
Spatial Association ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Real Data ◽  
Kernel Functions ◽  
Support Vector ◽  
Classification Problems ◽  
Rare Class ◽  
Data Adaptive

Imbalanced data exist in many classification problems. The classification of imbalanced data has remarkable challenges in machine learning. The support vector machine (SVM) and its variants are popularly used in machine learning among different classifiers thanks to their flexibility and interpretability. However, the performance of SVMs is impacted when the data are imbalanced, which is a typical data structure in the multi-category classification problem. In this paper, we employ the data-adaptive SVM with scaled kernel functions to classify instances for a multi-class population. We propose a multi-class data-dependent kernel function for the SVM by considering class imbalance and the spatial association among instances so that the classification accuracy is enhanced. Simulation studies demonstrate the superb performance of the proposed method, and a real multi-class prostate cancer image dataset is employed as an illustration. Not only does the proposed method outperform the competitor methods in terms of the commonly used accuracy measures such as the F-score and G-means, but also successfully detects more than 60% of instances from the rare class in the real data, while the competitors can only detect less than 20% of the rare class instances. The proposed method will benefit other scientific research fields, such as multiple region boundary detection.

scholarly journals A COMPARISON STUDY OF DIFFERENT KERNEL FUNCTIONS FOR SVM-BASED CLASSIFICATION OF MULTI-TEMPORAL POLARIMETRY SAR DATA

2014 ◽  
Vol XL-2/W3 ◽  
pp. 281-285 ◽  
Author(s):  
B. Yekkehkhany ◽  
A. Safari ◽  
S. Homayouni ◽  
M. Hasanlou
Keyword(s):  
Kernel Function ◽  
Temporal Integration ◽  
Experimental Tests ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Rbf Kernel ◽  
Multi Temporal ◽  
Dimension Space

In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). <br><br> The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

scholarly journals An Explainable Bayesian Decision Tree Algorithm

2021 ◽  
Vol 7 ◽  
Author(s):  
Giuseppe Nuti ◽  
Lluís Antoni Jiménez Rugama ◽  
Andreea-Ingrid Cross
Keyword(s):  
Monte Carlo ◽  
Decision Tree ◽  
Decision Trees ◽  
Medical Industry ◽  
Data Sets ◽  
Bayesian Decision ◽  
Decision Tree Algorithm ◽  
Probabilistic Framework ◽  
Classification Problems ◽  
Tree Algorithm

Bayesian Decision Trees provide a probabilistic framework that reduces the instability of Decision Trees while maintaining their explainability. While Markov Chain Monte Carlo methods are typically used to construct Bayesian Decision Trees, here we provide a deterministic Bayesian Decision Tree algorithm that eliminates the sampling and does not require a pruning step. This algorithm generates the greedy-modal tree (GMT) which is applicable to both regression and classification problems. We tested the algorithm on various benchmark classification data sets and obtained similar accuracies to other known techniques. Furthermore, we show that we can statistically analyze how was the GMT derived from the data and demonstrate this analysis with a financial example. Notably, the GMT allows for a technique that provides explainable simpler models which is often a prerequisite for applications in finance or the medical industry.

Research of a New SVM Kernel Function

2014 ◽  
Vol 543-547 ◽  
pp. 1659-1662
Author(s):  
Juan Du ◽  
Wen Long Zhang ◽  
Meng Meng Xie
Keyword(s):  
Kernel Function ◽  
Homogeneous Polynomial ◽  
Information Filtering ◽  
Kernel Functions ◽  
Learning Ability ◽  
New Combination ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Text Information ◽  
Information Recognition

The kernel was the key technology of SVM; the kernel affected the learning ability and generalization ability of support vector machine. Aiming at the specific application of harmful text information recognition, combining traditional kernel function the paper structured a new combination kernel, modeling for the independent harmful vocabulary and co-occur vocabularies, and then evaluation the linear kernel, homogeneous polynomial kernel, non homogeneous polynomial kernel and combination kernel function in the sample experiment. The experimental results of combination kernel function showed that the effect has increased greatly than other kernel functions for the application of harmful text information filtering. Especially the Rcall value achieved satisfactory results.

scholarly journals Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 667
Author(s):  
Wismaji Sadewo ◽  
Zuherman Rustam ◽  
Hamidah Hamidah ◽  
Alifah Roudhoh Chusmarsyah
Keyword(s):  
Machine Learning ◽  
Pancreatic Cancer ◽  
Support Vector Machine ◽  
Early Detection ◽  
Feature Space ◽  
Kernel Functions ◽  
Twin Support Vector Machine ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Rbf Kernel

Early detection of pancreatic cancer is difficult, and thus many cases of pancreatic cancer are diagnosed late. When pancreatic cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect pancreatic cancer early. This paper proposes a machine learning approach with the twin support vector machine (TWSVM) method as a new approach to detecting pancreatic cancer early. TWSVM aims to find two symmetry planes such that each plane has a distance close to one data class and as far as possible from another data class. TWSVM is fast in building a model and has good generalizations. However, TWSVM requires kernel functions to operate in the feature space. The kernel functions commonly used are the linear kernel, polynomial kernel, and radial basis function (RBF) kernel. This paper uses the TWSVM method with these kernels and compares the best kernel for use by TWSVM to detect pancreatic cancer early. In this paper, the TWSVM model with each kernel is evaluated using a 10-fold cross validation. The results obtained are that TWSVM based on the kernel is able to detect pancreatic cancer with good performance. However, the best kernel obtained is the RBF kernel, which produces an accuracy of 98%, a sensitivity of 97%, a specificity of 100%, and a running time of around 1.3408 s.

CREDIT SCORING USING MULTI-KERNEL SUPPORT VECTOR MACHINE AND CHAOS PARTICLE SWARM OPTIMIZATION

2012 ◽  
Vol 11 (03) ◽  
pp. 1250019 ◽  
Author(s):  
YUN LING ◽  
QIUYAN CAO ◽  
HUA ZHANG
Keyword(s):  
Particle Swarm Optimization ◽  
Kernel Function ◽  
Credit Scoring ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Classification Problem ◽  
Classification Performance ◽  
Kernel Functions ◽  
Support Vector ◽  
Swarm Optimization

Consumer credit scoring is considered as a crucial issue in the credit industry. SVM has been successfully utilized for classification in many areas including credit scoring. Kernel function is vital when applying SVM to classification problem for enhancing the prediction performance. Currently, most of kernel functions used in SVM are single kernel functions such as the radial basis function (RBF) which has been widely used. On the basis of the existing kernel functions, this paper proposes a multi-kernel function to improve the learning and generalization ability of SVM by integrating several single kernel functions. Chaos particle swarm optimization (CPSO) which is a kind of improved PSO algorithm is utilized to optimize parameters and to select features simultaneously. Two UCI credit data sets are used as the experimental data to evaluate the classification performance of the proposed method.

Image Segmentation Based on Least Squares Support Vector Machines

2014 ◽  
Vol 644-650 ◽  
pp. 4314-4318
Author(s):  
Xin You Wang ◽  
Ya Li Ning ◽  
Xi Ping He
Keyword(s):  
Image Segmentation ◽  
Support Vector Machines ◽  
Least Squares ◽  
Color Image ◽  
Pso Algorithm ◽  
Kernel Functions ◽  
Color Image Segmentation ◽  
Support Vector ◽  
Standard Data ◽  
Vector Machines

In order to solve the problem of the conventional methods operated directly in the image, difficult to obtain good results because they are poor in high dimension performance. In this paper, a new method was proposed, which use the Least Squares Support Vector Machines in image segmentation. Furthermore, the parameters of kernel functions are also be optimized by Particle Swarm Optimization (PSO) algorithm. The practical application in various of standard data sets and color image segmentation experiment. The results show that, LS-SVM can use a variety of features in image, the experiments have achieved good results of image segmentation, and the time needed for segmentation is greatly reduced than standard SVM.

Multiclass Contour-Preserving Classification with Support Vector Machine (SVM)

2017 ◽  
Vol 26 (2) ◽  
pp. 323-334 ◽  
Author(s):  
Piyabute Fuangkhon
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
University Of California ◽  
Support Vector ◽  
Data Sets ◽  
Feed Forward Neural Network ◽  
Real World Data ◽  
Data Set ◽  
The University ◽  
Training Sets

AbstractMulticlass contour-preserving classification (MCOV) has been used to preserve the contour of the data set and improve the classification accuracy of a feed-forward neural network. It synthesizes two types of new instances, called fundamental multiclass outpost vector (FMCOV) and additional multiclass outpost vector (AMCOV), in the middle of the decision boundary between consecutive classes of data. This paper presents a comparison on the generalization of an inclusion of FMCOVs, AMCOVs, and both MCOVs on the final training sets with support vector machine (SVM). The experiments were carried out using MATLAB R2015a and LIBSVM v3.20 on seven types of the final training sets generated from each of the synthetic and real-world data sets from the University of California Irvine machine learning repository and the ELENA project. The experimental results confirm that an inclusion of FMCOVs on the final training sets having raw data can improve the SVM classification accuracy significantly.

scholarly journals RESEARCH ON CROP INFORMATION EXTRACTION OF AGRICULTURAL UAV IMAGES BASED ON BLIND IMAGE DEBLURRING TECHNOLOGY AND SVM

2021 ◽  
pp. 33-42
Author(s):  
Zehai Xu ◽  
Haiyan Song ◽  
Zhiming Wu ◽  
Zefu Xu ◽  
Shifang Wang
Keyword(s):  
Information Extraction ◽  
Recognition Rate ◽  
Objective Evaluation ◽  
Kernel Functions ◽  
The Other ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Evaluation Indexes ◽  
Crop Classification

The blurring of crop images acquired by agricultural Unmanned Aerial Vehicle (UAV) due to sudden inputs by operators, atmospheric disturbance, and many other factors will eventually affect the subsequent crop identification, information extraction, and yield estimation. Aiming at the above problems, the new proposed combined deblurring algorithm based on the re-weighted graph total variation (RGTV) and L0-regularized prior, and the other two representative deblurring algorithms were applied to restore blurry crop images acquired during UAV flight, respectively. The restoration performance was measured by subjective vision, and objective evaluation indexes. The crop shape-related and texture-related feature parameters were then extracted, the Support Vector Machine (SVM) classifier with four common kernel functions was implemented for crop classification to realize the purpose of crop information extraction. The deblurring results showed that the proposed algorithm performed better in suppressing the ringing effect and preserving the image fine details, and retained higher objective evaluation indexes than the other two deblurring algorithms. The comparative analysis of different classification kernel functions showed that the Polynomial kernel function with an average recognition rate of 94.83% was most suitable for crop classification and recognition. The research will help in further popularization of crop monitoring based on UAV low-altitude remote sensing.

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