scholarly journals An empirical assessment of different kernel functions on the performance of support vector machines

2021 ◽  
Vol 10 (6) ◽  
pp. 3403-3411
Author(s):  
Isaac Kofi Nti ◽  
Owusu Nyarko-Boateng ◽  
Felix Adebayo Adekoya ◽  
Benjamin Asubam Weyori
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
High Accuracy ◽  
Kernel Functions ◽  
Support Vector ◽  
Technological Advancement ◽  
Specific Domain ◽  
The Earth ◽  
Vector Machines ◽  
Rbf Kernel

Artificial intelligence (AI) and machine learning (ML) have influenced every part of our day-to-day activities in this era of technological advancement, making a living more comfortable on the earth. Among the several AI and ML algorithms, the support vector machine (SVM) has become one of the most generally used algorithms for data mining, prediction and other (AI and ML) activities in several domains. The SVM’s performance is significantly centred on the kernel function (KF); nonetheless, there is no universal accepted ground for selecting an optimal KF for a specific domain. In this paper, we investigate empirically different KFs on the SVM performance in various fields. We illustrated the performance of the SVM based on different KF through extensive experimental results. Our empirical results show that no single KF is always suitable for achieving high accuracy and generalisation in all domains. However, the gaussian radial basis function (RBF) kernel is often the default choice. Also, if the KF parameters of the RBF and exponential RBF are optimised, they outperform the linear and sigmoid KF based SVM method in terms of accuracy. Besides, the linear KF is more suitable for the linearly separable dataset.

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.

scholarly journals Comparison some of kernel functions with support vector machines classifier for thalassemia dataset

2021 ◽  
Vol 10 (2) ◽  
pp. 430
Author(s):  
Ilsya Wirasati ◽  
Zuherman Rustam ◽  
Jane Eva Aurelia ◽  
Sri Hartini ◽  
Glori Stephani Saragih
Keyword(s):  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Medical Field ◽  
Treatment Technology ◽  
Blood Disorder ◽  
Vector Machines ◽  
Improve Accuracy ◽  
Rbf Kernel

<span id="docs-internal-guid-9a30056f-7fff-8ff1-59e1-69f89f4280bd"><span>In the medical field, accurate classification of medical data is really important because of its impact on disease detection and patient’s treatment. Technology, machine learning, is needed to help medical staff to improve accuracy to classify disease. This research discussed some kernel functions, such as gaussian radial basis function (RBF) kernel, Polynomial kernel, and linear kernel with support vector machine (SVM) to classify thalassemia data. Thalassemia is a genetic blood disorder which is also one of the major public health problems. In this paper, there is an explanation about thalassemia, SVM, and some of the kernel functions that serve as a comprehensive source for the next research about this topic. Furthermore, there is a comparison result from three kernel functions to find out which one has the best performance. The result is gaussian RBF kernel with SVM is the best method with an average of accuracy 99,63%. </span></span>

Analysis of regression prediction model of competitive sports based on SVM and artificial intelligence

2020 ◽  
Vol 39 (4) ◽  
pp. 5859-5869
Author(s):  
Jun Wang ◽  
Hongjun Qu
Keyword(s):  
Artificial Intelligence ◽  
Prediction Model ◽  
Optimization Method ◽  
Kernel Functions ◽  
Support Vector ◽  
Competitive Sports ◽  
Statistical Efficiency ◽  
Image Capture ◽  
Rbf Kernel ◽  
Speed Prediction

The training effect is not only affected by many environmental disturbance factors, but also related to various factors such as the athlete itself. In this paper, the author analyze the regression prediction model of competitive sports based on SVM and artificial intelligence. Traditional statistical modeling simply compares existing data between players and compares them between data. Moreover, it is unable to formulate corresponding tactical strategies according to the situation of the opponent, and targeted training to strengthen the level of individual sports skills.By com-paring the effects of several kernel functions on the SVM modeling side, it is found that the RBF kernel function can make the SVM’s prediction performance the best when dealing with the speed prediction problem. The experimental results show that this parameter optimization method can significantly improve the performance of the SVM regression machine. The prediction model based on support vector machine can effectively improve the prediction direction. Using artificial intelligence and image capture technology in sports can effectively improve the statistical efficiency and prediction effect of competition.

scholarly journals On the classification of arrhythmia using supplementary features from Tetrolet transforms

2019 ◽  
Vol 9 (6) ◽  
pp. 5006
Author(s):  
G. Jayagopi ◽  
S. Pushpa
Keyword(s):  
Heart Diseases ◽  
Classification Performance ◽  
Kernel Functions ◽  
Support Vector ◽  
Ecg Signals ◽  
The People ◽  
Vector Machines ◽  
Rbf Kernel ◽  
Electrocardiogram Ecg

<span>Heart diseases had been molded as potential threats to human lives, especially to elderly people in recent days due to the dynamically varying food habits among the people. However, these diseases could be easily caught by proper analysis of Electrocardiogram (ECG) signals acquired from individuals. This paper proposes a better method to detect and classify the arrhythmia using 15 features which include 4 R-R interval features, 3 statistical and 6 chaotic features estimated from ECG signals. Additionally, Entropy and Energy features had been gained after converting one dimensional ECG signals to two dimensional data and applied Tetrolet transforms on that.  Total numbers of 15 features had been utilized to classify the heart beats from the benchmark MIT-Arrhythmia database using Support Vector Machines (SVM). The classification performance was analyzed under various kernel functions and different Tetrolet decomposition levels. It is found that Radial Basis Function (RBF) kernel could perform better than linear and polynomial kernels. This research attempt yielded an accuracy of 99.35 % against the existing works. Moreover, addition of two more features had introduced a negligible overhead of time. Hence, this method is better suitable to detect and classify the Arrhythmia in both online and offline.</span>

Fundamentals of Machine Learning

2020 ◽  
Author(s):  
Floris Ernst ◽  
Achim Schweikard
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Linear Programming ◽  
Natural Sciences ◽  
Support Vector ◽  
Private Lives ◽  
Vector Machines ◽  
And Mathematics ◽  
Smo Algorithm

Artificial intelligence will change our lives forever - both at work and in our private lives. But how exactly does machine learning work? Two professors from Lübeck explore this question. In their English textbook they teach the necessary basics for the use of Support Vector Machines, for example, by explaining linear programming, the Lagrange multiplier, kernels and the SMO algorithm. They also deal with neural networks, evolutionary algorithms and Bayesian networks. Definitions are highlighted in the book and tasks invite readers to actively participate. The textbook is aimed at students of computer science, engineering and natural sciences, especially in the fields of robotics, artificial intelligence and mathematics.

scholarly journals The effect of gamma value on support vector machine performance with different kernels

2020 ◽  
Vol 10 (5) ◽  
pp. 5497
Author(s):  
Intisar Shadeed Al-Mejibli ◽  
Jwan K. Alwan ◽  
Dhafar Hamed Abd
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Kernel Functions ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Svm Classifier ◽  
Machine Performance ◽  
Rbf Kernel ◽  
The Impact

Currently, the support vector machine (SVM) regarded as one of supervised machine learning algorithm that provides analysis of data for classification and regression. This technique is implemented in many fields such as bioinformatics, face recognition, text and hypertext categorization, generalized predictive control and many other different areas. The performance of SVM is affected by some parameters, which are used in the training phase, and the settings of parameters can have a profound impact on the resulting engine’s implementation. This paper investigated the SVM performance based on value of gamma parameter with used kernels. It studied the impact of gamma value on (SVM) efficiency classifier using different kernels on various datasets descriptions. SVM classifier has been implemented by using Python. The kernel functions that have been investigated are polynomials, radial based function (RBF) and sigmoid. UC irvine machine learning repository is the source of all the used datasets. Generally, the results show uneven effect on the classification accuracy of three kernels on used datasets. The changing of the gamma value taking on consideration the used dataset influences polynomial and sigmoid kernels. While the performance of RBF kernel function is more stable with different values of gamma as its accuracy is slightly changed.

scholarly journals A novel artificial intelligence-based approach for identification of deoxynucleotide aptamers

2021 ◽  
Vol 17 (8) ◽  
pp. e1009247
Author(s):  
Frances L. Heredia ◽  
Abiel Roche-Lima ◽  
Elsie I. Parés-Matos
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Support Vector Machines ◽  
Dna Binding ◽  
Dna Sequences ◽  
Support Vector ◽  
Sequence Information ◽  
Dna Aptamers ◽  
Vector Machines ◽  
Selection Of

The selection of a DNA aptamer through the Systematic Evolution of Ligands by EXponential enrichment (SELEX) method involves multiple binding steps, in which a target and a library of randomized DNA sequences are mixed for selection of a single, nucleotide-specific molecule. Usually, 10 to 20 steps are required for SELEX to be completed. Throughout this process it is necessary to discriminate between true DNA aptamers and unspecified DNA-binding sequences. Thus, a novel machine learning-based approach was developed to support and simplify the early steps of the SELEX process, to help discriminate binding between DNA aptamers from those unspecified targets of DNA-binding sequences. An Artificial Intelligence (AI) approach to identify aptamers were implemented based on Natural Language Processing (NLP) and Machine Learning (ML). NLP method (CountVectorizer) was used to extract information from the nucleotide sequences. Four ML algorithms (Logistic Regression, Decision Tree, Gaussian Naïve Bayes, Support Vector Machines) were trained using data from the NLP method along with sequence information. The best performing model was Support Vector Machines because it had the best ability to discriminate between positive and negative classes. In our model, an Accuracy (A) of 0.995, the fraction of samples that the model correctly classified, and an Area Under the Receiving Operating Curve (AUROC) of 0.998, the degree by which a model is capable of distinguishing between classes, were observed. The developed AI approach is useful to identify potential DNA aptamers to reduce the amount of rounds in a SELEX selection. This new approach could be applied in the design of DNA libraries and result in a more efficient and faster process for DNA aptamers to be chosen during SELEX.

scholarly journals Machine learning based modelling for estimation of the fundamental time period of precast concrete structures using computer programming

2021 ◽  
Vol 30 (2) ◽  
Author(s):  
NITIN DAHIYA
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Gaussian Process ◽  
Precast Concrete ◽  
Concrete Structures ◽  
Kernel Functions ◽  
Support Vector ◽  
Learning Approaches ◽  
Time Period ◽  
Vector Machines

This research investigated the capability of machine learning approaches to evaluate the fundamental time period (FTP) of precast concrete structures. Data set consisting of 288 models with shear wall and beam-column frame structures. The 288 models were analysed using Etabs software and Rstudio.  Input parameters consisted of the height of the building, number of bays, length and breadth of the building, cracked or uncracked section, number of storeys and frame type on the FTP of precast concrete structures. Out of 288 models, for testing 108 arbitrary selected models were used and the remaining 180 models were used for training. Linear (LRF), polynomial (PLF) and radial basis (RBF) kernel functions were used for machine learning approach i.e support vector machines (SVM) and gaussian process (GPR). Evaluation of results suggests that linear function-based support vector machines performed well as compared to gaussian process regression. The accuracy of the machine learning approaches was verified through comparison with the available equations to evaluate the FTP in literature. 

Research on Snoring Recognition Algorithms

2019 ◽  
Vol 31 (1) ◽  
pp. 70-77
Author(s):  
Yongping Dan ◽  
Yaming Song ◽  
Dongyun Wang ◽  
Fenghui Zhang ◽  
Wei Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Endpoint Detection ◽  
Accuracy Rate ◽  
Vector Machines ◽  
Extraction Algorithm ◽  
Speech Endpoint Detection

A snoring recognition algorithm based on machine learning is proposed to effectively and precisely recognize snoring. To obtain a dataset, the speech endpoint detection algorithm and Mel frequency cepstrum coefficient feature extraction algorithm are applied to process speech signal samples. The dataset is classified into snoring and nonsnoring data (other speech signals) using support vector machines. Experimental results show that the algorithm recognizes snoring signals with a high accuracy rate of 97% and positively impacts subsequent research and related engineering applications.

The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques

2018 ◽  
Vol 7 (1) ◽  
pp. 72
Author(s):  
Khalid AA Abakar ◽  
Chongwen Yu
Keyword(s):  
Data Mining ◽  
Kernel Functions ◽  
Support Vector ◽  
Ann Model ◽  
Data Mining Techniques ◽  
Yarn Quality ◽  
Yarn Properties ◽  
Svm Model ◽  
Rbf Kernel

This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.

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