scholarly journals Research on Key Algorithms of the Lung CAD System Based on Cascade Feature and Hybrid Swarm Intelligence Optimization for MKL-SVM

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jiayue Chang ◽  
Yang Li ◽  
Hewei Zheng
Keyword(s):  
Swarm Intelligence ◽  
Convex Combination ◽  
Lung Nodule ◽  
Optimal Solution ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Hybrid Swarm ◽  
Cad System ◽  
Svm Algorithm ◽  
Swarm Intelligence Optimization

Feature selection and lung nodule recognition are the core modules of the lung computer-aided detection (Lung CAD) system. To improve the performance of the Lung CAD system, algorithmic research is carried out for the above two parts, respectively. First, in view of the poor interpretability of deep features and the incomplete expression of clinically defined handcrafted features, a feature cascade method is proposed to obtain richer feature information of nodules as the final input of the classifier. Second, to better map the global characteristics of samples, the multiple kernel learning support vector machine (MKL-SVM) algorithm with a linear convex combination of polynomial kernel and sigmoid kernel is proposed. Furthermore, this paper applied the methods for speed contraction factor and roulette strategy, and a mixture of simulated annealing (SA) and particle swarm optimization (PSO) is used for global optimization, so as to solve the problem that the PSO is easy to lose particle diversity and fall into the local optimal solution as well as improve the model’s training speed. Therefore, the MKL-SVM algorithm is presented in this paper, which is based on swarm intelligence optimization is proposed for lung nodule recognition. Finally, the algorithm construction experiments are conducted on the cooperative hospital dataset and compared with 8 advanced algorithms on the public dataset LUNA16. The experimental results show that the proposed algorithms can improve the accuracy of lung nodule recognition and reduce the missed detection of nodules.

scholarly journals The Weighted Support Vector Machine Based on Hybrid Swarm Intelligence Optimization for Icing Prediction of Transmission Line

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaomin Xu ◽  
Dongxiao Niu ◽  
Peng Wang ◽  
Yan Lu ◽  
Huicong Xia
Keyword(s):  
Support Vector Machine ◽  
Transmission Line ◽  
Swarm Intelligence ◽  
Hunan Province ◽  
Support Vector ◽  
Ice Thickness ◽  
Hybrid Swarm ◽  
Generalization Ability ◽  
Standard Support Vector Machine ◽  
Swarm Intelligence Optimization

Not only can the icing coat on transmission line cause the electrical fault of gap discharge and icing flashover but also it will lead to the mechanical failure of tower, conductor, insulators, and others. It will bring great harm to the people’s daily life and work. Thus, accurate prediction of ice thickness has important significance for power department to control the ice disaster effectively. Based on the analysis of standard support vector machine, this paper presents a weighted support vector machine regression model based on the similarity (WSVR). According to the different importance of samples, this paper introduces the weighted support vector machine and optimizes its parameters by hybrid swarm intelligence optimization algorithm with the particle swarm and ant colony (PSO-ACO), which improves the generalization ability of the model. In the case study, the actual data of ice thickness and climate in a certain area of Hunan province have been used to predict the icing thickness of the area, which verifies the validity and applicability of this proposed method. The predicted results show that the intelligent model proposed in this paper has higher precision and stronger generalization ability.

scholarly journals A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity

Minerals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 159
Author(s):  
Nan Lin ◽  
Yongliang Chen ◽  
Haiqi Liu ◽  
Hanlin Liu
Keyword(s):  
Machine Learning ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Geochemical Data ◽  
Support Vector ◽  
Learning Models ◽  
Hyperparameter Optimization ◽  
Mineral Prospectivity ◽  
Swarm Intelligence Optimization ◽  
Machine Learning Models

Selecting internal hyperparameters, which can be set by the automatic search algorithm, is important to improve the generalization performance of machine learning models. In this study, the geological, remote sensing and geochemical data of the Lalingzaohuo area in Qinghai province were researched. A multi-source metallogenic information spatial data set was constructed by calculating the Youden index for selecting potential evidence layers. The model for mapping mineral prospectivity of the study area was established by combining two swarm intelligence optimization algorithms, namely the bat algorithm (BA) and the firefly algorithm (FA), with different machine learning models. The receiver operating characteristic (ROC) and prediction-area (P-A) curves were used for performance evaluation and showed that the two algorithms had an obvious optimization effect. The BA and FA differentiated in improving multilayer perceptron (MLP), AdaBoost and one-class support vector machine (OCSVM) models; thus, there was no optimization algorithm that was consistently superior to the other. However, the accuracy of the machine learning models was significantly enhanced after optimizing the hyperparameters. The area under curve (AUC) values of the ROC curve of the optimized machine learning models were all higher than 0.8, indicating that the hyperparameter optimization calculation was effective. In terms of individual model improvement, the accuracy of the FA-AdaBoost model was improved the most significantly, with the AUC value increasing from 0.8173 to 0.9597 and the prediction/area (P/A) value increasing from 3.156 to 10.765, where the mineral targets predicted by the model occupied 8.63% of the study area and contained 92.86% of the known mineral deposits. The targets predicted by the improved machine learning models are consistent with the metallogenic geological characteristics, indicating that the swarm intelligence optimization algorithm combined with the machine learning model is an efficient method for mineral prospectivity mapping.

scholarly journals Using a least squares support vector machine to estimate a local geometric geoid model

2014 ◽  
Vol 20 (2) ◽  
pp. 427-443 ◽  
Author(s):  
Szu-Pyng Kao ◽  
Chao-Nan Chen ◽  
Hui-Chi Huang ◽  
Yu-Ting Shen
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Curve Surface ◽  
Optimal Solution ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Geoid Model ◽  
Geoidal Undulation ◽  
Orthometric Heights

In this study, test-region global positioning system (GPS) control points exhibiting known first-order orthometric heights were employed to obtain the points of plane coordinates and ellipsoidal heights by using the real-time GPS kinematic measurement method. Plane-fitting, second-order curve-surface fitting, back-propagation (BP) neural networks, and least-squares support vector machine (LS-SVM) calculation methods were employed. The study includes a discussion on data integrity and localization, changing reference-point quantities and distributions to obtain an optimal solution. Furthermore, the LS-SVM was combined with local geoidal-undulation models that were established by researching and analyzing3 kernel functions. The results indicated that the overall precision of the local geometric geoidal-undulation values calculated using the radial basis function (RBF) and third-order polynomial kernel function was optimal and the root mean square error (RMSE) was approximately ± 1.5 cm. These findings demonstrated that the LS-SVM provides a rapid and practical method for determining orthometric heights and should serve as a valuable academic reference regarding local geoid models.

A novel two-stage hybrid swarm intelligence optimization algorithm and application

2012 ◽  
Vol 16 (10) ◽  
pp. 1707-1722 ◽  
Author(s):  
Wu Deng ◽  
Rong Chen ◽  
Bing He ◽  
Yaqing Liu ◽  
Lifeng Yin ◽  
...  
Keyword(s):  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Hybrid Swarm ◽  
Two Stage ◽  
Swarm Intelligence Optimization

scholarly journals Software Defect Prediction Based on Hybrid Swarm Intelligence and Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zhen Li ◽  
Tong Li ◽  
YuMei Wu ◽  
Liu Yang ◽  
Hong Miao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Swarm Intelligence ◽  
Hybrid Algorithm ◽  
Fitness Function ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Hybrid Swarm ◽  
Hyperparameter Optimization ◽  
Advantages And Disadvantages ◽  
Swarm Algorithm

In order to improve software quality and testing efficiency, this paper implements the prediction of software defects based on deep learning. According to the respective advantages and disadvantages of the particle swarm algorithm and the wolf swarm algorithm, the two algorithms are mixed to realize the complementary advantages of the algorithms. At the same time, the hybrid algorithm is used in the search of model hyperparameter optimization, the loss function of the model is used as the fitness function, and the collaborative search ability of the swarm intelligence population is used to find the global optimal solution in multiple local solution spaces. Through the analysis of the experimental results of six data sets, compared with the traditional hyperparameter optimization method and a single swarm intelligence algorithm, the model using the hybrid algorithm has higher and better indicators. And, under the processing of the autoencoder, the performance of the model has been further improved.

scholarly journals High Performance of Polynomial Kernel at SVM Algorithm for Sentiment Analysis

2018 ◽  
Vol 3 (2) ◽  
pp. 194
Author(s):  
Lailil Muflikhah ◽  
Dimas Joko Haryanto
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
High Performance ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Algorithm ◽  
Rbf Kernel ◽  
C Value ◽  
Parameter Values

Sentiment analysis is a text mining based on the opinion collection towards the review of online product. Support Vector Machine (SVM) is an algorithm of classification that applicable to review the analysis of product. The hyperplane kernel function of SVM has importance role to classify the certain category. Therefore, this research is address to investigate the performance between Polynomial and Radial Basis Function (RBF) kernel functions for sentiment analysis of review product. They are examined to 200 comments using 10-fold validation and various parameter values (learning rate, lambda, c value, epsilon and iteration). As general, the performance for polynomial kernel of 88.75% is slightly higher than RBF kernel of 83.25%.

A Novel Intelligent Modeling Method for Wood Drying Process

2011 ◽  
Vol 121-126 ◽  
pp. 647-651
Author(s):  
Dong Yan Zhang ◽  
Chun Yan Zhang ◽  
Liang Kuan Zhu ◽  
Zhi Duo Diao
Keyword(s):  
Swarm Intelligence ◽  
Parameters Optimization ◽  
Learning Ability ◽  
Support Vector ◽  
Drying Process ◽  
Wood Drying ◽  
Swarm Optimization ◽  
Nonlinear Characteristics ◽  
Intelligent Modeling ◽  
Swarm Intelligence Optimization

This paper investigates the development and intelligent modeling problem for a wood drying kiln process via optimized support vector machine (SVM). Based on parameters optimization and model selection idea, the swarm intelligence algorithms of Particle Swarm Optimization (PSO)-SVM and Genetic Algorithm (GA)-SVM were proposed for wood drying process with strong coupling and nonlinear characteristics. The simulation results showed that both of these two kinds of swarm intelligence optimization algorithm could get the appropriate parameters of SVM effectively, and by contrast, PSO showed a better learning ability and generalization in wood drying process modeling, and could establish predictive model with better accessibility.

scholarly journals A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
Daqing Wu ◽  
Jianguo Zheng
Keyword(s):  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Population Diversity ◽  
Function Optimization ◽  
Benchmark Problems ◽  
Particle Swarm Optimizer ◽  
Hybrid Swarm ◽  
Swarm Intelligence Optimization

A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO) and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC) for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.

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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