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Study on a Novel Data Classification Method Based on Improved GA and SVM Model

Jing Huo, Yuxiang Zhao
2016 International Journal of Smart Home  
Liu [5] proposed an active learning algorithm with support vector machine for performing active learning with support vector machine and applied the algorithm to gene expression profiles of colon cancer  ...  Chau et al. [22] proposed a novel method for SVM classification based on convex-concave hull and support vector machine, called convex-concave hull SVM (CCH-SVM).  ...  The program for the study, initialization, training, and simulation of the proposed algorithm in this article was written with the tool-box of MATLAB 2010b produced by the Math-Works.  ... 
doi:10.14257/ijsh.2016.10.5.12 fatcat:syos6hsgyfhwjjy22okuutn4mq

SVM Parameter Optimization Based on Immune Memory Clone Strategy and Application in Bus Passenger Flow Counting

Fang Zhu, Jun Fang Wei
2012 Advanced Engineering Forum  
According to the above deficiency, this paper proposed a parameters optimization method of support vector machine based on immune memory clone strategy (IMC).  ...  The performance of support vector machine (SVM) depends on the selection of model parameters, however, the selection of SVM model parameters more depends on the empirical value.  ...  Parameters Optimization Algorithm of Support Vector Machine Based on Immune Memory Clone Strategy (IMC-SVM).  ... 
doi:10.4028/www.scientific.net/aef.6-7.694 fatcat:ptg3vui3qzhcxf5uekfz5fhdje

Differential Evolution Algorithm with Hierarchical Fair Competition Model

Amit Ramesh Khaparde, Fawaz Alassery, Arvind Kumar, Youseef Alotaibi, Osamah Ibrahim Khalaf, Sofia Pillai, Saleh Alghamdi
2022 Intelligent Automation and Soft Computing  
Experimentation has carried out to define the parameter for proposed model on test suit having unimodal problems and multi-model problems.  ...  The standard differential evolution algorithm is used for population evolution.  ...  Acknowledgement: We deeply acknowledge Taif University for supporting this study through Taif University Researchers Supporting Project Number (TURSP-2020/150), Taif University, Taif, Saudi Arabia.  ... 
doi:10.32604/iasc.2022.023270 fatcat:cua75vvfd5fxnl3poeti6khwzq

Feature Selection for Text and Image Data Using Differential Evolution with SVM and Naïve Bayes Classifiers

Abhishek Dixit, Ashish Mani, Rohit Bansal
2020 Engineering Journal  
The proposed approach uses differential evolution (DE) for feature selection with naïve bayes (NB) and support vector machine (SVM) classifiers to enhance the performance of selected classifier.  ...  To optimize such challenges, a hybrid approach is suggested in this paper.  ...  [27] in which is based on multi objective DE for feature selection. All these algorithms use classical DE approach where a fixed mutation strategy is used with tuning of control parameters.  ... 
doi:10.4186/ej.2020.24.5.161 fatcat:k5zjg45xubatnpyzfhv46lsi7i

Study on a Novel Hybrid Intelligent Fault Diagnosis Method Based on Improved DE and RBFNN

Liu Yi
2016 International Journal of Database Theory and Application  
for obtaining the improved DE(DASDE) algorithm.  ...  So an improved differential evolution algorithm based on dynamic adaptive adjustment strategy is proposed to optimize the parameters of RBFNN model for obtaining the optimal RBFNN (DASDERBFNN) method.  ...  obtaining the improved DE(DASDE) algorithm, which is used to optimize the parameters in the RBFNN for obtaining the better parameter optimization of the RBFNN.  ... 
doi:10.14257/ijdta.2016.9.8.16 fatcat:7ssul5iwzbfulcljg7onsagptq

Machinery Prognostic Method Based on Multi-Class Support Vector Machines and Hybrid Differential Evolution – Particle Swarm Optimization

J.K. Kimotho, C. Sondermann-Wolke, T. Meyer, W. Sextro
2013 Chemical Engineering Transactions  
The differential algorithm (DE) obtains the search limit for the SVM parameters, while the particle swarm optimization algorithm (PSO) determines the global optimum parameters for a given training data  ...  To address this problem, this paper proposes a hybrid differential evolution – particle swarm optimization (DE-PSO) algorithm to optimize the MC-SVM kernel function and penalty parameters.  ...  Acknowledgement The authors wish to acknowledge the support of the Chair, Department of Mechatronics and Dynamics, University of Paderborn, German Academic Exchange Service (DAAD) and the Ministry of Higher  ... 
doi:10.3303/cet1333104 doaj:56ca7a41531d47379b9ee8c19f1c2a44 fatcat:ilxngfqlmfevdplkm462v2ayby

Enhanced Differential Evolution Based on Adaptive Mutation and Wrapper Local Search Strategies for Global Optimization Problems

Chun-Liang Lu, Shih-Yuan Chiu, Chih-Hsu Hsu, Shi-Jim Yen
2014 Journal of Applied Research and Technology  
Differential evolution (DE) is a simple, powerful optimization algorithm, which has been widely used in many areas.  ...  In addition, the wide range of Pareto-optimal solutions and more Gantt chart decision-makings can be provided for the multi-objective FJSP combinatorial optimizations.  ...  Acknowledgments This work was partially supported by the National Science Council, Taiwan, under Grant NSC 101-2218-E-254-001.  ... 
doi:10.1016/s1665-6423(14)71672-4 fatcat:nrcsrxxqwvbjpj3e3hy3begkfa

Design and Identification Problems of Rotor Bearing Systems Using the Simulated Annealing Algorithm [chapter]

Fran Sergio, Elaine Gomes, Valder Steffen, Antonio Jose da Silva Neto
2012 Simulated Annealing - Single and Multiple Objective Problems  
Acknowledgement The authors acknowledge the financial support provided by FAPEMIG and CNPq (INCT-EIE). The fourth author is grateful to the financial support provided by CNPq and FAPERJ.  ...  • Multi-objective Optimization Differential Evolution (MODE) parameters [37] : population size (50), perturbation rate (0.8), crossover probability (0.8), DE/rand/1/bin strategy for the generation  ...  DE/rand/1/bin strategy. • Particle Swarm Optimization (PS) parameters [43]: population size (25), maximum velocity (100), upper limits (2.0), and a linearly decreasing inertia weight starting at 0.7  ... 
doi:10.5772/47833 fatcat:qcqavtolnjbtfpwkhxsn33gpem

A Brief Overview on Parameter Optimization of Support Vector Machine

Qi Cao, Lei Yu, Mingsheng Cheng
2017 DEStech Transactions on Materials Science and Engineering  
Support vector machine (SVM) has been successfully applied in classification and regression problems. But it is very sensitive to the selection of parameters.  ...  The objective of this paper is to provide readers a brief overview of the recent advances for parameter optimization of SVM and enable them to develop and implement new optimization strategies for SVM-related  ...  [34] proposed a novel hybrid optimization algorithm based on immunity clone (IC) and DE for parameter selection of SVM.  ... 
doi:10.12783/dtmse/smne2016/10603 fatcat:cyvzsrghmfgzdazdh7r52rce2a

A review on fault classification methodologies in power transmission systems: Part-II

Avagaddi Prasad, J. Belwin Edward, K. Ravi
2018 Journal of Electrical Systems and Information Technology  
For this purpose, the technical literature proposes a large number of methods.  ...  The part 2 of the article is named "A review on fault classification methodologies in power transmission systems".  ...  C.1 Support vector machine A novel technique for learning separating functions in classification (pattern recognition) tasks or for performing functional estimation in regression problems is support vector  ... 
doi:10.1016/j.jesit.2016.10.003 fatcat:zpupf7dirrbnxptelhxpsrhtci

State of the art of machine learning models in energy systems, a systematic review

Mohsen Salimi, Amir Mosavi, Sina Faizollahzadeh Ardabili, Majid Amidpour, Timon Rabczuk, Shahabodin Shamshirband
2020 Zenodo  
In particular, the last two decades has seen a dramatic increase in the development and application of various types of ML models for energy systems.  ...  algorithms.  ...  The quantum particle swarm optimization algorithm was utilized for optimizing the parameters of support vector regression.  ... 
doi:10.5281/zenodo.4056884 fatcat:yf6gjoevffc2xnzoyjlc3pxbii

Review of data mining applications for quality assessment in manufacturing industry: support vector machines

Hamidey Rostami, Jean-Yves Dantan, Lazhar Homri
2015 International Journal of Metrology and Quality Engineering  
, namely support vector machine (SVM) to solve QA problems.  ...  Although a few review papers have recently been published to discuss DM applications in manufacturing for QA, this paper provides an extensive review to investigate the application of a special DM technique  ...  They modeled machine training for SVM as a multi-parameter optimization problem which is solved by SEOA.  ... 
doi:10.1051/ijmqe/2015023 fatcat:eeimca6kibehrb7tlmrf7usjka

Review of Optimization in Improving Extreme Learning Machine

Nilesh Rathod, Sunil Wankhade
2021 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems  
Hence through this study, we intend to study the different algorithms developed for optimizing the ELM to enhance its performance in the aspects of survey criteria such as datasets, algorithm, objectives  ...  Now a days Extreme Learning Machine has gained a lot of interest because of its noteworthy qualities over single hiddenlayer feedforward neural networks and the kernel functions.  ...  Acknowledgement The paper "Improving Extreme Learning Machine through Optimization A Review." has been accepted in part for presentation at International Conference on Advanced Computing and Communication  ... 
doi:10.4108/eai.17-9-2021.170960 fatcat:tqm3lsk44zg2nbwf5wx4fzgdai

Development of accurate classification of heavenly bodies using novel machine learning techniques

Michał Wierzbiński, Paweł Pławiak, Mohamed Hammad, U. Rajendra Acharya
2021 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
Then, the genetic algorithm is implemented on the data which helps to find the optimal parameters for the classifiers.  ...  In this work, we are proposing to develop a novel machine learning model to classify stellar spectra of stars, quasars and galaxies.  ...  Acknowledgements Funding for the Sloan Digital Sky Survey IV has  ... 
doi:10.1007/s00500-021-05687-4 fatcat:eyv4lbanc5gzxkgsw5xzi2qc6q

Multi-objective evolution of artificial neural networks in multi-class medical diagnosis problems with class imbalance

Alex Shenfield, Shahin Rostami
2017 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)  
This paper proposes a novel multi-objective optimisation approach to solving both the problem of finding good structural and parametric choices in an ANN and the problem of training a classifier with a  ...  The state-of-the-art CMA-PAES-HAGA multi-objective evolutionary algorithm [41] is used to simultaneously optimise the structure, weights, and biases of a population of ANNs with respect to not only the  ...  This novel multi-objective approach not only addresses the problem of choosing optimal ANN topologies and parameters but, by considering trade-offs in classification performance between multiple target  ... 
doi:10.1109/cibcb.2017.8058553 dblp:conf/cibcb/ShenfieldR17 fatcat:3r2j4cdyirbwnl5e5j6sdp7a7e
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