Application of Cost-Sensitive Hybrid-Kernel Support Vector Machine (SVM) to Sentiment Analysis

2016 ◽  
Author(s):  
Guest Editor Jianping Du
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Support Vector ◽  
Kernel Support Vector Machine ◽  
Hybrid Kernel

A scheduling procedure for a flow shop–like knowledgeable manufacturing cell with self-evolutionary features

2016 ◽  
Vol 230 (12) ◽  
pp. 2296-2306 ◽  
Author(s):  
Hong-Sen Yan ◽  
Wen-Chao Li
Keyword(s):  
Support Vector Machine ◽  
Learning Process ◽  
Flow Shop ◽  
Learning Ability ◽  
Support Vector ◽  
Manufacturing Cells ◽  
Kernel Support Vector Machine ◽  
Evolutionary Features ◽  
Knowledgeable Manufacturing ◽  
Hybrid Kernel

As a component of knowledgeable manufacturing systems, the structure of flow shop–like knowledgeable manufacturing cells is similar to that of a flow shop, thus representing an NP-hard issue. Here, we propose a self-evolutionary algorithm that exhibits learning ability and is composed of learning and scheduling modules. Unlike traditional scheduling algorithms, whose performances remain unchanged when the procedure is coded, the performance of the algorithm proposed in this study gradually improves as the learning process continues. The self-evolutionary ability is realized through the training of a hybrid kernel support vector machine. The hybrid kernel support vector machine was designed to approximate the value of the Q-function to select the appropriate action for the scheduling module and thus to obtain the optimal solution. An iterative process of value based on the Q-learning was adopted to train the hybrid kernel support vector machine to gradually enhance the algorithm’s efficiency and accuracy. The extracted state features of the flow shop–like knowledgeable manufacturing cells serve as inputs to hybrid kernel support vector machine for easy generalization of the learning results. The action exerted on a feasible solution is also defined as the input of the hybrid kernel support vector machine. The computational results show that the performance of the proposed procedure improves as the learning process progresses. Data from the computation and comparisons with other algorithms verify the validity and efficiency of the proposed algorithm.

scholarly journals PERBANDINGAN KERNEL SUPPORT VECTOR MACHINE (SVM) DALAM PENERAPAN ANALISIS SENTIMEN VAKSINISASI COVID-19

2021 ◽  
Vol 4 (2) ◽  
pp. 139-145
Author(s):  
Thalita Meisya Permata Aulia ◽  
Nur Arifin ◽  
Rini Mayasari
Keyword(s):  
Clinical Trial ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Large Scale ◽  
Research Data ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Kernel Space ◽  
Kernel Support Vector Machine ◽  
Pros And Cons

In early 2020, the first recorded death from the COVID-19 virus in China [3]. Followed by WHO which later stated that the COVID-19 virus caused a pandemic. Various efforts were made to minimize the transmission of COVID-19, such as physical distancing and large-scale social circulation. However, this resulted in a paralyzed economy, many factories or business shops closed, eliminating the livelihoods of many people. Vaccines may be a solution, various International Research Communities have conducted research on the COVID-19 vaccine. In early 2021 the Sinovac vaccine from China arrived in Indonesia and was declared a BPOM clinical trial, but the existence of the vaccine still raises pros and cons, some have responded well and others have not. For this reason, a sentiment analysis of the COVID-19 vaccine will be carried out by taking data from Twitter, then classified using the Support Vector Machine algorithm. The research data is nonlinear data so it requires a kernel space for the text mining process, while there has been no specific research regarding which kernel is good for sentiment analysis, so a test will be carried out to find the best kernel among linear, sigmoid, polynomial, and RBF kernels. The result is that sigmoid and linear kernels have a better value, namely 0.87 compared to RBF and polynomial, namely 0.86

scholarly journals Study on Evaluation Model of Chinese P2P Online Lending Platform Based on Hybrid Kernel Support Vector Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Shuang Pan ◽  
Jianguo Wei ◽  
Hao Pan
Keyword(s):  
Support Vector Machine ◽  
Evaluation Model ◽  
Risk Level ◽  
Support Vector ◽  
Empirical Parameter ◽  
Kernel Support Vector Machine ◽  
Svm Model ◽  
Online Lending ◽  
Kernel Svm ◽  
Hybrid Kernel

Accurate evaluation of the risk level and operation performances of P2P online lending platforms is not only conducive to better functioning of information intermediaries but also effective protection of investors’ interests. This paper proposes a genetic algorithm (GA) improved hybrid kernel support vector machine (SVM) with an index system to construct such an evaluation model. A hybrid kernel consisting of polynomial function and radial basis function is improved, specifically kernel parameters and the weight of two kernels, by GA method with excellent global optimization and rapid convergence. Empirical testing based on cross-sectional data from Chinese P2P lending market demonstrates the superiority of the improved hybrid kernel SVM model. The classification accuracy of credit risk level and operation quality is higher than the single kernel SVM model as well as the hybrid kernel model with empirical parameter values.

Hybrid Kernel Support Vector Machine Classifier and Grey Wolf Optimization Algorithm Based Intelligent Classification Algorithm for Chronic Kidney Disease

2020 ◽  
Vol 10 (10) ◽  
pp. 2297-2307
Author(s):  
L. Jerlin Rubini ◽  
Eswaran Perumal
Keyword(s):  
Chronic Kidney Disease ◽  
Support Vector Machine ◽  
Kidney Disease ◽  
Classification Model ◽  
Support Vector ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Novel Approach ◽  
Kernel Support Vector Machine ◽  
Hybrid Kernel

In the present day, distributed algorithms become more popular due to their diversity in several applications. The prediction and reorganization of medical data required more practice and information. We propose a novel approach feature selection based on efficient chronic kidney disease (CKD) prediction and classification. Primarily, the pre-processing pace will be implemented over the input data. Then, the grey wolf optimization (GWO) algorithm gets executed to choose the optimal features from the pre-processed data. Next, the projected technique exploits the Hybrid Kernel Support Vector Machine (HKSVM) as a classification model to identify the presence of CKD or not. The simulation takes place in MATLAB. The validation of the presented model takes place using a benchmark CKD dataset as of machine learning repository such as UCI under the presence of several measures. New outcome specified that the planned categorization arrangement has surpassed by containing enhanced 97.26% accuracy for kidney chronic dataset when contrasted with existing SVM technique only accomplished 94.77% and fuzzy min–max GSO neural network (FMMGNN) classifier accomplished 93.78%.

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

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