Modeling Knowledge Employee’s Turnover Based on P-SVM

Knowledge employee’s turnover forecast is a multi-criteria decision-making problem involving various factors. In order to forecast accurately turnover of knowledge employees, the potential support vector machines(P-SVM) is introduced to develop a turnover forecast model. In the model development, a chaos algorithm and a genetic algorithm (GA) are employed to optimize P-SVM parameters selection. The simulation results show that the model based on potential support vector machine with chaos not only has much stronger generalization ability but also has the ability of feature selection.

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Regional-Scale Mineral Prospectivity Mapping: Support Vector Machines and an Improved Data-Driven Multi-criteria Decision-Making Technique

Natural Resources Research ◽

10.1007/s11053-021-09842-4 ◽

2021 ◽

Author(s):

Reza Ghezelbash ◽

Abbas Maghsoudi ◽

Amirreza Bigdeli ◽

Emmanuel John M. Carranza

Keyword(s):

Decision Making ◽

Support Vector Machines ◽

Regional Scale ◽

Data Driven ◽

Support Vector ◽

Multi Criteria Decision Making ◽

Mineral Prospectivity Mapping ◽

Vector Machines ◽

Mineral Prospectivity ◽

Prospectivity Mapping

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Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm

Journal of Zhejiang University SCIENCE A ◽

10.1631/jzus.2005.b0961 ◽

2005 ◽

Vol 6B (10) ◽

pp. 961-973 ◽

Cited By ~ 17

Author(s):

Yong Mao ◽

Xiao-bo Zhou ◽

Dao-ying Pi ◽

You-xian Sun ◽

Stephen T.C. Wong

Keyword(s):

Genetic Algorithm ◽

Support Vector Machines ◽

Gene Selection ◽

Gaussian Kernel ◽

Support Vector ◽

Parameters Selection ◽

Vector Machines ◽

Kernel Support Vector Machines

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Patent Innovation Factors Evolution Based on P-SVM and GCCA

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.39.247 ◽

2010 ◽

Vol 39 ◽

pp. 247-252

Author(s):

Sheng Xu ◽

Zhi Juan Wang ◽

Hui Fang Zhao

Keyword(s):

Genetic Algorithm ◽

Support Vector Machines ◽

Correlation Coefficient ◽

Network Architecture ◽

Innovation Management ◽

Model Development ◽

Real Data ◽

Support Vector ◽

Vector Machines ◽

Gray Correlation

A two-stage neural network architecture constructed by combining potential support vector machines (P-SVM) with genetic algorithm (GA) and gray correlation coefficient analysis (GCCA) is proposed for patent innovation factors evolution. The enterprises patent innovation is complex to conduct due to its nonlinearity of influenced factors. It is necessary to make a trade off among these factors when some of them conflict firstly. A novel way about nonlinear regression model with the potential support vector machines (P-SVM) is presented in this paper. In the model development, the genetic algorithm is employed to optimize P-SVM parameters selection. After the selected key factors by the PSVM with GA model, the main factors that affect patent innovation generation have been quantitatively studied using the method of gray correlation coefficient analysis. Using a set of real data in China, the results show that the methods developed in this paper can provide valuable information for patent innovation management and related municipal planning projects.

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A Method of Improved Support Vector Machine for Network Security Situation Forecasting

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.187.291 ◽

2011 ◽

Vol 187 ◽

pp. 291-296

Author(s):

Yuan Cheng Li ◽

Jing Tao Jing

Keyword(s):

Network Security ◽

Forecast Accuracy ◽

Forecast Model ◽

Selection Method ◽

Support Vector ◽

Swarm Optimization ◽

Parameters Selection ◽

Vector Machines ◽

Good Forecast ◽

Improved Support Vector Machine

Aiming at the problem that parameters of Support Vector Machines (SVM) are very difficult to confirm, this paper points out a parameter selection method for SVM based on Particle Swarm Optimization (PSO), which can make the SVM more scientific and reasonable in parameters selection; and thus enhance the forecast accuracy of the network security situation. The Simulation results show that the optimized SVR forecast model has good forecast accuracy for the network security situation, and present the future changing at a macro level, then help the network managers control network.

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Parameters Selection and Application of LS-SVM Based on Chaotic Ant Swarm Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.204-210.423 ◽

2011 ◽

Vol 204-210 ◽

pp. 423-426

Author(s):

Chun Li Xie ◽

Dan Dan Zhao ◽

Juan Wang ◽

Cheng Shao

Keyword(s):

Support Vector Machines ◽

Nonlinear Systems ◽

Fitness Function ◽

Selection Method ◽

Support Vector ◽

Mean Square ◽

Parameters Selection ◽

Vector Machines ◽

The Mean ◽

Simulation Results

Parameters selection plays an important role for the performance of least squares support vector machines (LS-SVM). In this paper, a novel parameters selection method for LS-SVM is presented based on chaotic ant swarm (CAS) algorithm. Using this method, the optimization model is established, within which the fitness function is the mean square error (MSE) index, and the constraints are the ranges of the designing parameters. The proposed method is used in the identification for inverse model of the nonlinear systems, and simulation results are given to show the efficiency.

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Modelling Soil Water Retention Using Support Vector Machines with Genetic Algorithm Optimisation

The Scientific World JOURNAL ◽

10.1155/2014/740521 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 7

Author(s):

Krzysztof Lamorski ◽

Cezary Sławiński ◽

Felix Moreno ◽

Gyöngyi Barna ◽

Wojciech Skierucha ◽

...

Keyword(s):

Genetic Algorithm ◽

Support Vector Machines ◽

Soil Water ◽

Water Retention ◽

Model Development ◽

Water Retention Curve ◽

Support Vector ◽

Retention Data ◽

Soil Water Retention ◽

Vector Machines

This work presents point pedotransfer function (PTF) models of the soil water retention curve. The developed models allowed for estimation of the soil water content for the specified soil water potentials: –0.98, –3.10, –9.81, –31.02, –491.66, and –1554.78 kPa, based on the following soil characteristics: soil granulometric composition, total porosity, and bulk density. Support Vector Machines (SVM) methodology was used for model development. A new methodology for elaboration of retention function models is proposed. Alternative to previous attempts known from literature, theν-SVM method was used for model development and the results were compared with the formerly used theC-SVM method. For the purpose of models’ parameters search, genetic algorithms were used as an optimisation framework. A new form of the aim function used for models parameters search is proposed which allowed for development of models with better prediction capabilities. This new aim function avoids overestimation of models which is typically encountered when root mean squared error is used as an aim function. Elaborated models showed good agreement with measured soil water retention data. Achieved coefficients of determination values were in the range 0.67–0.92. Studies demonstrated usability ofν-SVM methodology together with genetic algorithm optimisation for retention modelling which gave better performing models than other tested approaches.

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Gait and Posture Analysis Method Based on Genetic Algorithm and Support Vector Machines with Acceleration Data

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2016.p0418 ◽

2016 ◽

Vol 28 (3) ◽

pp. 418-424 ◽

Cited By ~ 3

Author(s):

Huan Gou ◽

◽

Tengda Shi ◽

Lei Yan ◽

Jiang Xiao

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Parameters Optimization ◽

Support Vector ◽

Analysis Method ◽

Posture Analysis ◽

Acceleration Data ◽

Vector Machines ◽

Posture Recognition ◽

Simulation Results

[abstFig src='/00280003/18.jpg' width=""300"" text='The result of parameters optimization by GA' ] The support vector machine (SVM) we propose for automated gait and posture recognition is based on acceleration. Acceleration data are obtained from four accelerators attached to the human thigh and lower leg. In the experiment, volunteers take part in four gaits and postures, i.e., sitting, standing, walking and ascending stairs. Acceleration data that are preprocessed include normalization, a wavelet filter and dimension reduction. We used the SVM and a neural network to analyze the data processed. Simulation results indicate that SVM parametersCandgselected by a genetic algorithm (GA) are more effective for gait and posture analysis when compared to the parameterCandgselected by a grid search. The overall classification precision of the four gaits and postures exceeds 90.0%, and neural network simulation results indicate that the SVM using the GA is preferable for use in analysis.

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