scholarly journals Comparison between fuzzy kernel k-medoids using radial basis function kernel and polynomial kernel function in hepatitis classification

2021 ◽  
Vol 10 (1) ◽  
pp. 60
Author(s):  
Glori Stephani Saragih ◽  
Sri Hartini ◽  
Zuherman Rustam
Keyword(s):  
Radial Basis Function ◽  
Kernel Function ◽  
Basis Function ◽  
Cross Validation ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Radial Basis ◽  
Rbf Kernel ◽  
Rbf Kernel Function ◽  
Fold Cross Validation

<span id="docs-internal-guid-10508d4e-7fff-5011-7a0e-441840e858c8"><span>This paper compares the fuzzy kernel k-medoids using radial basis function (RBF) and polynomial kernel function in hepatitis classification. These two kernel functions were chosen due to their popularity in any kernel-based machine learning method for solving the classification task. The hepatitis dataset then used to evaluate the performance of both methods that were expected to provide an accurate diagnosis in patients to obtain treatment at an early phase. The data were obtained from two hospitals in Indonesia, consisting of 89 hepatitis-B and 31 hepatitis-C samples. The data were analyzed using several cases of k-fold cross-validation, and the performances were compared according to their accuracy, sensitivity, precision, F1-Score, and running time. From the experiments, it was concluded that fuzzy kernel k-medoids using RBF kernel function is better compared to polynomial kernel function with the 6% increment of accuracy, 13% enhancement of sensitivity, and 5% improvement in F1-Score. On the other side, the precision of fuzzy kernel k-medoids using polynomial kernel function is 2% higher than using the RBF kernel function. According to the results, the use of RBF or polynomial kernel function in fuzzy kernel medoids can be considered according to the primary goal of the classification.</span></span>

Bearing Binary Classification Intelligent Diagnosis by Combined Improved EEMD with SVM

2013 ◽  
Vol 341-342 ◽  
pp. 1066-1070
Author(s):  
Mei Jun Zhang ◽  
Jie Huang ◽  
Kai Chai ◽  
Hao Chen
Keyword(s):  
Kernel Function ◽  
Binary Classification ◽  
Kernel Functions ◽  
Outer Ring ◽  
Polynomial Kernel ◽  
Ring Fault ◽  
Rbf Kernel ◽  
Inner Circle ◽  
Rbf Kernel Function ◽  
Improved Eemd

In order to perform the bearing intelligent fault diagnosis,combined improved EEMD with SVM respectively applied to the binary classification identification of bearing normal and ball fault, normal and inner circle fault,normal and outer ring fault in this paper.Improve EEMD decomposed 9d normalized energy for characteristic vector,the SVM binary classification and recognition of bearings normal and ball fault, normal and inner circle fault, normal and outer ring fault is researched.Compared to the SVM classification accuracy using different kernel functions that is linear kernel function, polynomial kernel function, RBF kernel function and Sigmoid kernel function.In the same parameters,SVM classification accuracy based on linear kernel function and polynomial kernel function is a hundred percent.Bearing normal and ball fault,normal and inner circle fault,normal and outer ring fault is completely correct apart.And there are the classification errors based on RBF kernel function and Sigmoid kernel functions.

Evaluate the Performance of SVM Kernel Functions for Multiclass Cancer Classification

2020 ◽  
Vol 1 (1) ◽  
pp. 37-41
Author(s):  
Noramalina Mohd Hatta ◽  
Zuraini Ali Shah ◽  
Shahreen Kasim
Keyword(s):  
Kernel Function ◽  
Supervised Classification ◽  
Kernel Functions ◽  
Cancer Classification ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Linear Kernel ◽  
Straight Line ◽  
Rbf Kernel ◽  
Rbf Kernel Function

Multiclass cancer classification is basically one of the challenging fields in machine learning which a fast growing technology that use human behaviour as examples. Supervised classification such Support Vector Machine (SVM) has been used to classify the dataset on classification by its own function and merely known as kernel function. Kernel function has stated to have a problem especially in selecting their best kernels based on a specific datasets and tasks. Besides, there is an issue stated that the kernels function have a high impossibility to distribute the data in straight line. Here, three basic kernel functions was used and tested with selected dataset and they are linear kernel, polynomial kernel and Radial Basis Function (RBF) kernel function. The three kernels were tested by different dataset to gain the accuracy. For a comparison, this study conducting a test by with and without feature selection in SVM classification kernel function since both tests will give different result and thus give a big meaning to the study.

Optimization of process parameters in plastic injection molding for minimizing the volumetric shrinkage and warpage using radial basis function (RBF) coupled with the k-fold cross validation technique

2019 ◽  
Vol 39 (5) ◽  
pp. 481-492 ◽  
Author(s):  
Behzad Shiroud Heidari ◽  
Amin Hedayati Moghaddam ◽  
Seyed Mohammad Davachi ◽  
Shadi Khamani ◽  
Afshar Alihosseini
Keyword(s):  
Injection Molding ◽  
Radial Basis Function ◽  
Process Parameters ◽  
Basis Function ◽  
Cross Validation ◽  
Volumetric Shrinkage ◽  
Process Conditions ◽  
Radial Basis ◽  
Validation Technique ◽  
Fold Cross Validation

Abstract In this study, a multi-objective design optimization method based on a radial basis function (RBF) model was applied to minimize the volumetric shrinkage and warpage of hip liners as an injection-molded biomedical part. The hip liners included an ultrahigh molecular weight polyethylene (UHMWPE) liner and UHMWPE reinforced with a nano-hydroxyapatite (nHA) liner. The shrinkage and warpage values of the hip liners were generated by simulation of the injection molding process using Autodesk Moldflow. The RBF model was used to build an approximate function relationship between the objectives and the process parameters. The process parameters, including mold temperature, melt temperature, injection time, packing time, packing pressure, coolant temperature, and type of liner, were surveyed to find the interaction effects of them on the shrinkage and warpage of the liners. The results indicated that the addition of nHA helps the liners to obtain more dimensional stability. The model was validated by the k-fold cross validation technique. Finally, the model revealed the optimal process conditions to achieve the minimized shrinkage and warpage simultaneously for various weights.

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.

APPLICATION OF GENERALIZED RADIAL BASIS FUNCTION NETWORKS TO RECOGNITION OF RADAR TARGETS

1999 ◽  
Vol 13 (06) ◽  
pp. 945-962 ◽  
Author(s):  
DE-SHUANG HUANG
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Classification Performance ◽  
Kernel Functions ◽  
Radial Basis Function Networks ◽  
One Dimensional ◽  
Parzen Window ◽  
Window Functions ◽  
Radial Basis ◽  
Radar Targets

This paper extends general radial basis function networks (RBFN) with Gaussian kernel functions to generalized radial basis function networks (GRBFN) with Parzen window functions, and discusses applying the GRBFNs to recognition of radar targets. The equivalence between the RBFN classifiers (RBFNC) with outer-supervised signals of 0 or 1 and the estimate of Parzen windowed probabilistic density is proved. It is pointed out that the I/O functions of the hidden units in the RBFNC can be extended to general Parzen window functions (or called as potential functions). We present using recursive least square-backpropagation (RLS–BP) learning algorithm to train the GRBFNCs to classify five types of radar targets by means of their one-dimensional cross profiles. The concepts about the rate of recognition and confidence in the process of testing classification performance of the GRBFNCs are introduced. Six generalized kernel functions such as Gaussian, Double-Exponential, Triangle, Hyperbolic, Sinc and Cauchy, are used as the hidden I/O functions of the RBFNCs, and the classification performance of corresponding GRBFNCs for classifying one-dimensional cross profiles of radar targets is discussed.

scholarly journals Financial Risk Early Warning Based on Wireless Network Communication and the Optimal Fuzzy SVM Artificial Intelligence Model

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yong Ma ◽  
Hao Liu ◽  
Guangyu Zhai ◽  
Zongjie Huo
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machine ◽  
Radial Basis Function ◽  
Wireless Network ◽  
Basis Function ◽  
Financial Risk ◽  
Listed Companies ◽  
Kernel Functions ◽  
Support Vector ◽  
Radial Basis

Since the beginning of the new century, risk events such as the world economic crisis have occurred, which have greatly impacted the real economy of our country. A wireless network is a network implemented using wireless communication technology. It includes both global voice and data networks that allow users to establish long-distance wireless connections, as well as infrared technology and radio frequency technology optimized for short-distance wireless connections. These events have a great impact on many small- and medium-sized listed companies, resulting to many small- and medium-sized listed companies going bankrupt. Indeed, one of the important reasons for the frequent bankruptcy of small- and medium-sized listed companies is the lack of awareness of risk prevention and effective financial risk early warning mechanism. The support vector machine is a machine learning method based on the VC dimension theory of statistical learning and the principle of structural risk minimization. This method shows many unique advantages when dealing with classification problems and has been widely used in many fields. The purpose of this article is to realize the financial risk analysis of listed companies through wireless network communication and the optimal fuzzy SVM artificial intelligence model, which help small- and medium-sized listed companies find abnormalities in their business management activities in advance and deal with market risks in a timely manner. Taking 81 small- and medium-sized listed companies as the research object, this paper chooses the small- and medium-sized listed companies in every quarter of 2018 as the research sample. By using the financial and nonfinancial data of small- and medium-sized listed companies and introducing the support vector machine (SVM) with the fuzzy method, the model of the fuzzy support vector machine (FSVM) is constructed. And the performance of the FSVM under four different kernel functions is compared and studied. At the same time, the performance of the FSVM is compared with other artificial intelligence models. The empirical results show that different kernel functions have different effects on the prediction performance of the FCM-SVM model. Under the Gauss radial basis function, the prediction accuracy of the FCM-SVM is over 86%. It can be seen that in predicting the financial crisis of small- and medium-sized listed companies, the FCM-SVM model with Gauss radial basis function has the best predictive performance. The FSVM model based on Gauss radial basis function not only has the advantages of linearity, being polynomial, and nonlinearity of neurons but also is significantly superior to the traditional artificial intelligence model.

scholarly journals Unified Noise Reduction using Adaptive Radial Basis Function

2019 ◽  
Vol 8 (9) ◽  
pp. 484-490
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Noise Reduction ◽  
Kernel Function ◽  
Basis Function ◽  
Gaussian Noise ◽  
Speckle Noise ◽  
Gaussian Kernel ◽  
Noisy Image ◽  
Radial Basis

The images captured by SAR and sonar are blurred and corrupted more by speckle noise and also other types of noise like Gaussian noise and salt & pepper noise. Denoising all types of noises to get perfect image is a vital challenge, earlier works on the same mode addressed with one filter for one noise, there is no one common or unified filter which can denoise all types of noise. Therefore in this paper, we have designed a filter which not only removes speckle noise, but also combination of other noises. Here IUNR (Intelligent Unified Noise Reduction) algorithm is proposed which is based on neural network called adaptive radial basis function acts as a unified filter for Denoising. Proposed method needs a single noisy image to train the adaptive radial basis function neural network to learn the correction of the noisy image. The Gaussian kernel function is applied to reconstruct the local disturbance appeared because of the noise. The proposed adaptive radial basis function network is compared with the fixed form which has fixed spread and the center value of kernel function. This method can correct the image suffered from different varieties of noises like speckle noise, salt & pepper noise and Gaussian noise separately or combination of noise. Various standard test images are considered for test purpose with different levels of noise density and performance of proposed algorithm is compared with adaptive wiener filter.

scholarly journals A Novel Artificial Intelligence Technique to Estimate the Gross Calorific Value of Coal Based on Meta-Heuristic and Support Vector Regression Algorithms

2019 ◽  
Vol 9 (22) ◽  
pp. 4868 ◽  
Author(s):  
Hoang-Bac Bui ◽  
Hoang Nguyen ◽  
Yosoon Choi ◽  
Xuan-Nam Bui ◽  
Trung Nguyen-Thoi ◽  
...  
Keyword(s):  
Radial Basis Function ◽  
Support Vector Regression ◽  
Basis Function ◽  
Calorific Value ◽  
Kernel Functions ◽  
Support Vector ◽  
Gross Calorific Value ◽  
Heating Value ◽  
Geological Conditions ◽  
Radial Basis

Gross calorific value (GCV) is one of the essential parameters for evaluating coal quality. Therefore, accurate GCV prediction is one of the primary ways to improve heating value as well as coal production. A novel evolutionary-based predictive system was proposed in this study for predicting GCV with high accuracy, namely the particle swarm optimization (PSO)-support vector regression (SVR) model. It was developed based on the SVR and PSO algorithms. Three different kernel functions were employed to establish the PSO-SVR models, including radial basis function, linear, and polynomial functions. Besides, three benchmark machine learning models including classification and regression trees (CART), multiple linear regression (MLR), and principle component analysis (PCA) were also developed to estimate GCV and then compared with the proposed PSO-SVR model; 2583 coal samples were used to analyze the proximate components and GCV for this study. Then, they were used to develop the mentioned models as well as check their performance in experimental results. Root-mean-squared error (RMSE), correlation coefficient (R2), ranking, and intensity color criteria were used and computed to evaluate the GCV predictive models developed. The results revealed that the proposed PSO-SVR model with radial basis function had better accuracy than the other models. The PSO algorithm was optimized in the SVR model with high efficiency. These should be used as a supporting tool in practical engineering to determine the heating value of coal seams in complex geological conditions.

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