Classification Algorithm of Regression Support Vector Machine and its Application to Environment Monitoring in Water Culture Plants

2013 ◽  
Vol 427-429 ◽  
pp. 1121-1127 ◽  
Author(s):  
Man Fu Yan ◽  
Jiu Hai Wang
Keyword(s):  
Loss Function ◽  
Optimization Problem ◽  
Open Data ◽  
Support Vector ◽  
Environment Monitoring ◽  
Data Set ◽  
Water Culture ◽  
Monitoring Result ◽  
New Type ◽  
Better Than

In this paper, it applies Gaussian loss function instead of ε-insensitive loss function in a standard SVRM to devise a new model and a new type of support vector classification machine whose optimization problem is easier to solve and has conducted effective test on open data set in order to apply the new algorithm to environment monitoring in water culture plants and the monitoring result is better than any other method available.

A Computational Method for the Identification of Endolysins and Autolysins

2020 ◽  
Vol 27 (4) ◽  
pp. 329-336 ◽  
Author(s):  
Lei Xu ◽  
Guangmin Liang ◽  
Baowen Chen ◽  
Xu Tan ◽  
Huaikun Xiang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Cell Wall ◽  
Experimental Results ◽  
Computational Method ◽  
Lytic Enzyme ◽  
Support Vector ◽  
Lytic Enzymes ◽  
Data Set ◽  
Optimal Feature ◽  
Better Than

Background: Cell lytic enzyme is a kind of highly evolved protein, which can destroy the cell structure and kill the bacteria. Compared with antibiotics, cell lytic enzyme will not cause serious problem of drug resistance of pathogenic bacteria. Thus, the study of cell wall lytic enzymes aims at finding an efficient way for curing bacteria infectious. Compared with using antibiotics, the problem of drug resistance becomes more serious. Therefore, it is a good choice for curing bacterial infections by using cell lytic enzymes. Cell lytic enzyme includes endolysin and autolysin and the difference between them is the purpose of the break of cell wall. The identification of the type of cell lytic enzymes is meaningful for the study of cell wall enzymes. Objective: In this article, our motivation is to predict the type of cell lytic enzyme. Cell lytic enzyme is helpful for killing bacteria, so it is meaningful for study the type of cell lytic enzyme. However, it is time consuming to detect the type of cell lytic enzyme by experimental methods. Thus, an efficient computational method for the type of cell lytic enzyme prediction is proposed in our work. Method: We propose a computational method for the prediction of endolysin and autolysin. First, a data set containing 27 endolysins and 41 autolysins is built. Then the protein is represented by tripeptides composition. The features are selected with larger confidence degree. At last, the classifier is trained by the labeled vectors based on support vector machine. The learned classifier is used to predict the type of cell lytic enzyme. Results: Following the proposed method, the experimental results show that the overall accuracy can attain 97.06%, when 44 features are selected. Compared with Ding's method, our method improves the overall accuracy by nearly 4.5% ((97.06-92.9)/92.9%). The performance of our proposed method is stable, when the selected feature number is from 40 to 70. The overall accuracy of tripeptides optimal feature set is 94.12%, and the overall accuracy of Chou's amphiphilic PseAAC method is 76.2%. The experimental results also demonstrate that the overall accuracy is improved by nearly 18% when using the tripeptides optimal feature set. Conclusion: The paper proposed an efficient method for identifying endolysin and autolysin. In this paper, support vector machine is used to predict the type of cell lytic enzyme. The experimental results show that the overall accuracy of the proposed method is 94.12%, which is better than some existing methods. In conclusion, the selected 44 features can improve the overall accuracy for identification of the type of cell lytic enzyme. Support vector machine performs better than other classifiers when using the selected feature set on the benchmark data set.

DIALOG ACT CLASSIFICATION USING ACOUSTIC AND DISCOURSE INFORMATION OF MAPTASK DATA

2010 ◽  
Vol 09 (04) ◽  
pp. 289-311 ◽  
Author(s):  
FATEMA N. JULIA ◽  
KHAN M. IFTEKHARUDDIN ◽  
ATIQ U. ISLAM
Keyword(s):  
Classifier Fusion ◽  
Support Vector ◽  
Acoustic Features ◽  
Average Precision ◽  
Data Set ◽  
Parts Of Speech ◽  
Pos Tagging ◽  
Accuracy Rates ◽  
Better Than

Dialog act (DA) classification is useful to understand the intentions of a human speaker. An effective classification of DA can be exploited for realistic implementation of expert systems. In this work, we investigate DA classification using both acoustic and discourse information for HCRC MapTask data. We extract several different acoustic features and exploit these features using a Hidden Markov Model (HMM) network to classify acoustic information. For discourse feature extraction, we propose a novel parts-of-speech (POS) tagging technique that effectively reduces the dimensionality of discourse features. To classify discourse information, we exploit two classifiers such as a HMM and Support Vector Machine (SVM). We further obtain classifier fusion between HMM and SVM to improve discourse classification. Finally, we perform an efficient decision-level classifier fusion for both acoustic and discourse information to classify 12 different DAs in MapTask data. We obtain 65.2% and 55.4% DA classification rates using acoustic and discourse information, respectively. Furthermore, we obtain combined accuracy of 68.6% for DA classification using both acoustic and discourse information. These accuracy rates of DA classification are either comparable or better than previously reported results for the same data set. For average precision and recall, we obtain accuracy rates of 74.89% and 69.83%, respectively. Therefore, we obtain much better precision and recall rates for most of the classified DAs when compared to existing works on the same HCRC MapTask data set.

PSO-tuned support vector machine metamodels for assessment of turbulent flows in pipe bends

2019 ◽  
Vol 37 (3) ◽  
pp. 981-1001 ◽  
Author(s):  
Ganesh Narayanan ◽  
Milan Joshi ◽  
Prasun Dutta ◽  
Kanak Kalita
Keyword(s):  
Support Vector Machine ◽  
Fluid Flow ◽  
Loss Function ◽  
Parametric Study ◽  
Large Scale ◽  
Mean Squared Error ◽  
Support Vector ◽  
Data Set ◽  
Content Type ◽  
Cfd Models

Purpose Computational fluid dynamics (CFD) technique is the most commonly used numerical approach to simulate fluid flow behaviour. Owing to its computationally, cost-intensive nature CFD models may not be easily and quickly deployable. In this regard, this study aims to present a support vector machine (SVM)-based metamodelling approach that can be easily trained and quickly deployed for carrying out large-scale studies. Design/methodology/approach Radial basis function and ε^*-insensitive loss function are used as kernel function and loss function, respectively. To prevent overfitting of the model, five-fold cross-validation root mean squared error is used while training the SVM metamodel. Rather than blindly using any SVM tuning parameters, a particle swarm optimisation (PSO) is used to fine-tune them. The developed SVM metamodel is tested using various error metrics on disjoint test data. Findings Using the SVM metamodel, a parametric study is conducted to understand the effect of various factors influencing the behaviour of the turbulent fluid flow in the pipe bend with CFD simulation data set. Based on the parametric study carried out, it is seen that the diametric position has the most effect on dimensionless axial velocity, whereas Reynolds number has the least effect. Originality/value This paper provides an effective PSO-tuned SVM metamodelling approach, which may be used as a significant cost-saving approach to quickly and accurately estimate fluid flow characteristics that, in general, require the use of expensive CFD models.

scholarly journals Statistical inference for Kumaraswamy-exponential distribution based on progressive Type-II censored data with binomial removals

2020 ◽  
Vol 53 (2) ◽  
pp. 147-163
Author(s):  
RAKHI MOHAN ◽  
MANOJ CHACKO
Keyword(s):  
Exponential Distribution ◽  
Loss Function ◽  
Real Data ◽  
Maximum Likelihood Estimates ◽  
Type Ii ◽  
Bayes Estimators ◽  
Estimation Of Parameters ◽  
Unknown Parameters ◽  
Data Set ◽  
Better Than

In this paper, estimation of parameters of Kumaraswamy-exponential distribution with shape parameters α and β is considered based on a progressively type-II censored sample with binomial removals. Together with the unknown parameters, the removal probability p is also estimated. Bayes estimators are obtained using different loss functions such as squared error, LINEX loss function and entropy loss function. All Bayesian estimates are compared with the corresponding maximum likelihood estimates numerically in terms of their bias and mean square error values and found that Bayes estimators perform better than MLE’s for β and p and MLEs perform better than Bayes estimators for α. A real data set is also used for illustration.

A Novel Robust Smooth Support Vector Machine

2011 ◽  
Vol 148-149 ◽  
pp. 1438-1441 ◽  
Author(s):  
Shao Chao Sun ◽  
Dao Huang
Keyword(s):  
Support Vector Machine ◽  
Loss Function ◽  
Unconstrained Minimization ◽  
Support Vector ◽  
Smooth Approximation ◽  
Fast Learning ◽  
Learning Speed ◽  
New Type ◽  
Simulation Results ◽  
Smooth Support Vector Machine

In this paper, we propose a new type of ε-insensitive loss function, called as ε-insensitive Fair estimator. With this loss function we can obtain better robustness and sparseness. To enhance the learning speed ,we apply the smoothing techniques that have been used for solving the support vector machine for classification, to replace the ε-insensitive Fair estimator by an accurate smooth approximation. This will allow us to solve ε-SFSVR as an unconstrained minimization problem directly. Based on the simulation results, the proposed approach has fast learning speed and better generalization performance whether outliers exist or not.

scholarly journals FAKE NEWS DETECTION USING VARIOUS MACHINE LEARNING TECHNIQUES

2021 ◽  
pp. 134-137
Author(s):  
Akanksha Akulwar ◽  
Anish Khobragade
Keyword(s):  
Machine Learning ◽  
High Accuracy ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Fake News ◽  
Data Set ◽  
Online Platforms ◽  
Learning Techniques ◽  
Original Classification ◽  
Better Than

Online platforms are being used for outspreading malicious talk, which creates an impact on the minds of millions. Many distinct approaches have been brought forward to detect this fake news, but very few have been carried out in the actual world. We address this problem of estimating the rumor authentication in a real-world in less time with significantly high accuracy. We design and implement an approach addressing the above issue. We accessed whether the news is fake or not using various Machine learning techniques. We evaluate this algorithm on a set of data set scrapped from random online sites. The result shows that the performance of this improved algorithm is better than the original classification method. And finally, we consider various sizes of data to view and compare the accuracy. KEYWORDS—Support Vector Machine, Decision Tree, Fake Content, Naive Bayes, Machine Learning Models

scholarly journals Performance analysis of sentiments in Twitter dataset using SVM models

2021 ◽  
Vol 11 (3) ◽  
pp. 2275
Author(s):  
Lakshmana Kumar Ramasamy ◽  
Seifedine Kadry ◽  
Yunyoung Nam ◽  
Maytham N. Meqdad
Keyword(s):  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Product Reviews ◽  
Discussion Forums ◽  
Data Set ◽  
Vector Machines ◽  
The Individual ◽  
Radial Kernel ◽  
Better Than

Sentiment Analysis is a current research topic by many researches using supervised and machine learning algorithms. The analysis can be done on movie reviews, twitter reviews, online product reviews, blogs, discussion forums, Myspace comments and social networks. The Twitter data set is analyzed using support vector machines (SVM) classifier with various parameters. The content of tweet is classified to find whether it contains fact data or opinion data. The deep analysis is required to find the opinion of the tweets posted by the individual. The sentiment is classified in to positive, negative and neutral. From this classification and analysis, an important decision can be made to improve the productivity. The performance of SVM radial kernel, SVM linear grid and SVM radial grid was compared and found that SVM linear grid performs better than other SVM models.

scholarly journals Improving the Efficiency of Heart Disease Prediction Using SVM and a Novel Tree Specific Random Forest Classifier (NTSRF)

2021 ◽  
Vol 36 (1) ◽  
pp. 616-622
Author(s):  
P. Harish ◽  
Dr.R. Sabitha
Keyword(s):  
Heart Disease ◽  
Random Forest ◽  
Random Forest Classifier ◽  
Support Vector ◽  
Svm Classifier ◽  
Disease Prediction ◽  
Data Set ◽  
Medical Sector ◽  
Accuracy And Precision ◽  
Better Than

Aim: The objective of the work is to evaluate the accuracy and precision in predicting the heart disease using Support Vector Machine (SVM) and Random Forest (RF) classification algorithms. Materials and Methods: Random Forest Classifier is applied on a Health dataset that consists of 304 records. A framework for heart disease prediction in the medical sector comparing Random Forest and SVM classifiers has been proposed and developed. The sample size was measured as 21 per group. The accuracy and the precision of the classifiers was evaluated and recorded. Results: The SVM classifier produces 53.04% in predicting the heart disease on the data set used whereas the Random forest classifier predicts the same at the rate of 83.2%. The significant value is 0.0. Hence RF is better than SVM. Conclusion: The performance of Random forest is better compared with SVM in terms of both precision and accuracy.

Support Vector Regression Optimization Problem without Bias

2012 ◽  
Vol 23 (9) ◽  
pp. 2336-2346
Author(s):  
Xiao-Jian DING ◽  
Yin-Liang ZHAO
Keyword(s):  
Support Vector Regression ◽  
Optimization Problem ◽  
Support Vector

Evaluation for estimating of the PDF and the CDF of Generalized Inverted Exponential Distribution with Application in Industry

2020 ◽  
pp. 507-522
Author(s):  
Parisa Torkaman
Keyword(s):  
Least Squares ◽  
Exponential Distribution ◽  
Mean Squared Error ◽  
Weighted Least Squares ◽  
Real Data ◽  
Minimum Variance ◽  
Cumulative Distribution ◽  
Estimation Methods ◽  
Data Set ◽  
Better Than

The generalized inverted exponential distribution is introduced as a lifetime model with good statistical properties. This paper, the estimation of the probability density function and the cumulative distribution function of with five different estimation methods: uniformly minimum variance unbiased(UMVU), maximum likelihood(ML), least squares(LS), weighted least squares (WLS) and percentile(PC) estimators are considered. The performance of these estimation procedures, based on the mean squared error (MSE) by numerical simulations are compared. Simulation studies express that the UMVU estimator performs better than others and when the sample size is large enough the ML and UMVU estimators are almost equivalent and efficient than LS, WLS and PC. Finally, the result using a real data set are analyzed.

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