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Fuzzy Cost Support Vector Regression

Hadi Sadoghi Yazdi, Tahereh Royani, Mehri Sadoghi Yazdi, Sohrab Effati
2008 Zenodo  
In this paper, a new version of support vector regression (SVR) is presented namely Fuzzy Cost SVR (FCSVR).  ...  Individual property of the FCSVR is operation over fuzzy data whereas fuzzy cost (fuzzy margin and fuzzy penalty) are maximized.  ...  THE PROPOSED FUZZY COST SUPPORT VECTOR REGRESSION (FCSVR) In this section, we discuss the proposed algorithm for support vector regression, termed as fuzzy cost support vector regression (FCSVR).  ... 
doi:10.5281/zenodo.1081820 fatcat:iismsn2tmvahbiulepm7uto3iy

A support vector-based interval type-2 fuzzy system

Volkan Uslan, Huseyin Seker, Robert John
2014 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)  
In the proposed model the consequent parameters of type-2 fuzzy rules are learnt using support vector regression and the computational cost is reduced with the use of a closed-form type reduction.  ...  In this paper, a new fuzzy regression model that is supported by support vector regression is presented. Type-2 fuzzy systems are able to tackle applications that have significant uncertainty.  ...  As to avoid overfitting, support vector regression (SVR) can be an alternative regression approach and leads to generalisation as compared to least-squares estimation for the fuzzy systems.  ... 
doi:10.1109/fuzz-ieee.2014.6891813 dblp:conf/fuzzIEEE/UslanSJ14 fatcat:gvilyz4i7bgcbffkvobhcqylhe

Interpretable support vector regression

Tamás Kenesei, János Abonyi
2012 Artificial intelligence research  
This paper deals with transforming Support vector regression (SVR) models into fuzzy systems (FIS).  ...  It is highlighted that trained support vector based models can be used for the construction of fuzzy rule-based regression models.  ...  With the combination of visualization and interpretation the black-box support vector regression is identified in one step.  ... 
doi:10.5430/air.v1n2p11 fatcat:nshclvndcfembki3yao2etcsmq

An Epsilon Hierarchical Fuzzy Twin Support Vector Regression [article]

Arindam Chaudhuri
2015 arXiv   pre-print
The research presents epsilon hierarchical fuzzy twin support vector regression based on epsilon fuzzy twin support vector regression and epsilon twin support vector regression.  ...  The structural risk minimization principle is implemented by introducing regularization term in primal problems of epsilon FTSVR.  ...  -HIERARCHICAL FUZZY TWIN SUPPORT VECTOR REGRESSION Based on -FTSVR, -HFTSVR is formulated here.  ... 
arXiv:1509.03247v1 fatcat:jjlnw2pmjvhzhfvzft6w3mlc7y

A New Hybrid Intelligent Algorithm for Fuzzy Multiobjective Programming Problem Based on Credibility Theory

Zu-Tong Wang, Jian-Sheng Guo, Ming-Fa Zheng, Ying Wang
2014 Mathematical Problems in Engineering  
For solving the fuzzy MOP problem efficiently, Latin hypercube sampling, fuzzy simulation, support vector machine, and artificial bee colony algorithm are integrated to build a hybrid intelligent algorithm  ...  Based on the credibility theory, this paper is devoted to the fuzzy multiobjective programming problem.  ...  Acknowledgments This work was supported by the National Natural Science Foundation under Grant no. 71171199 and the Natural Science Foundation of Shaanxi Province under Grant no. 2013JM1003.  ... 
doi:10.1155/2014/909203 fatcat:vz4eq42alffzhbnn3wpedy5w4i

A novel improved fuzzy support vector machine based stock price trend forecast model [article]

Shuheng Wang, Guohao Li, Yifan Bao
2018 arXiv   pre-print
If the training of support vector machine contains noise and fuzzy information, the performance of the support vector machine will become very weak and powerless.  ...  Novel advanced- fuzzy support vector machine (NA-FSVM) is the proposed methodology.  ...  Diverse rebalance heuristics in support vector machines displaying, including cost-sensitive learning, and over and under sampling has been proposed.  ... 
arXiv:1801.00681v1 fatcat:v7g7u4knkndplhzalxvhyckww4

The Last State of Artificial Intelligence in Project Management [article]

Mohammad Reza Davahli
2020 arXiv   pre-print
However, the most popular PM processes among included papers were project effort prediction and cost estimation, and the most popular AI techniques were support vector machines, neural networks, and genetic  ...  This paper reports on a systematic review of the published studies used to investigate the application of AI in PM.  ...  Fuzzy decision trees [17] Planning Cost Cost estimation Linear regression, support vector regression (SVR), and multilayer perceptron (MLP) [18] Planning Cost Cost estimation Genetic algorithm  ... 
arXiv:2012.12262v1 fatcat:q3qxxsb6rbailg7leuoputgphy

A Support Vector Classifier Based on Vague Similarity Measure

Yong Zhang, Jing Cai
2013 Mathematical Problems in Engineering  
This paper proposes a support vector classifer based on vague sigmoid kernel and its similarity measure.  ...  Support vector machine (SVM) is a popular machine learning method for its high generalizaiton ability.  ...  The first one is fuzzy support vector machine (FSVM) [2] [3] [4] .  ... 
doi:10.1155/2013/928054 fatcat:sna6k5zxufe5lj7k6sfkcq24km

Comparison of Artificial Intelligence Techniques for Project Conceptual Cost Prediction [article]

Haytham H. Elmousalami
2019 arXiv   pre-print
The study focuses on investigating twenty artificial intelligence (AI) techniques which are conducted for cost modeling such as fuzzy logic (FL) model, artificial neural networks (ANNs), multiple regression  ...  However, these methods are unable to produce accurate results for conceptual cost prediction due to small and unstable data samples.  ...  The study has applied the radial base function (RBF) as a kernel for Supportive vector regression model.  ... 
arXiv:1909.11637v1 fatcat:fmqedswdzzegdfblyuukvq5le4


Shahram Hosseinzadeh, Mehdi Shaghaghi
2020 Progress In Electromagnetics Research M  
as a fuzzy support vector machine.  ...  To this end, three neural network based fuzzy support vectors are used to determine the soil, depth, and dimensions.  ...  ACKNOWLEDGMENT this work was supported by the East Azarbaijan National Gas Company of Iran under contraction No. 082349.  ... 
doi:10.2528/pierm20050805 fatcat:7hqnplmte5gfphlskormbmwtlm

Credit Risk Assessment Modeling Method Based on Fuzzy Integral and SVM

Mingyi Zhou, Chia-Huei Wu
2022 Mobile Information Systems  
In view of the shortcomings of BP neural network in the establishment of credit risk assessment model, such as poor promotion ability and long prediction time, and considering that support vector machine  ...  This paper discusses the structure and algorithm principle of SVM classification method and proposes an integrated SVM based on fuzzy integral to solve this kind of problem.  ...  effect after integration (4) The classification results of integrated support vector machine based on fuzzy integral, traditional integrated support vector machine based on voting method, single support  ... 
doi:10.1155/2022/3950210 fatcat:2tk6owrjzveilikblmacp2tcae

Support-Vector-Based Fuzzy Neural Networks

Chin-Teng Lin, Chang-Mao Yeh, Jen-Feng Chung, Sheng-Fu Liang, Her-Chang Pu
2005 International Journal of Computational Intelligence Research  
Keywords : Fuzzy neural network, fuzzy kernel function, support vector 32 Chin-Teng Lin et al machine, support vector regression, pattern classification, function approximation.  ...  In this paper, novel fuzzy neural networks (FNNs) combining with support vector learning mechanism called support-vector-based fuzzy neural networks (SVFNNs) are proposed for pattern classification and  ...  Acknowledgment This work was supported in part by the Ministry of Education, Taiwan, R.O.C., under  ... 
doi:10.5019/j.ijcir.2005.31 fatcat:s4clyb7errhxxkqftam2xgxzmm

Object Oriented Software Usability Estimate with Adaptive Neuro Fuzzy, Fuzzy Svm

Mohammad Saber Iraji, Reyhane mosaddegh
2013 International Journal of Information Engineering and Electronic Business  
In this paper, we present many intelligent models to estimate the usability of object oriented software.  ...  In our proposed system, fuzzy svm has less errors and system worked more accurate and appropriative than prior methods.  ...  ACKNOWLEDGMENT This work received support from the Department of Computer Engineering and Information Technology, Payame Noor University ,I.R. of Iran.  ... 
doi:10.5815/ijieeb.2013.01.05 fatcat:oe54zl4e5bevnhukrpt4rzbnvq

Emphatic Constraints Support Vector Machine

Mostafa Sabzekar, Hadi Sadoghi Yazdi, Mahmoud Naghibzadeh, Sohrab Effati
2010 International Journal of Computer and Electrical Engineering  
In this paper, a new support vector machine, ESVM, with more emphasis on constraints is presented. The constraints are fuzzy inequalities.  ...  ESVM and fuzzy ESVM are strongly recommended to the researchers who work on data sets with noisy or low degree of certainty samples.  ...  ACKNOWLEDGEMENT This work has been partially supported by Iran Telecommunication Research Center (ITRC), Tehran, Iran. This support is gratefully acknowledged.  ... 
doi:10.7763/ijcee.2010.v2.152 fatcat:tpziw5iisjaw7bqr5tr7kyx6fy

A New Fast Algorithm for Fuzzy Rule Selection

Barbara Pizzileo, Kang Li
2007 IEEE International Fuzzy Systems conference proceedings  
This paper investigates the selection of fuzzy rules for fuzzy neural networks.  ...  This is achieved by the proposal of a fast forward rule selection algorithm (FRSA), where the rules are selected one by one and a residual matrix is recursively updated in calculating the contribution  ...  ,m, icpr is the jth vector of the matrix ip u 2) Update of the regression context. From (12) j+lau'r -(+Rr)jpl) (j+'R-rl))(j?  ... 
doi:10.1109/fuzzy.2007.4295633 dblp:conf/fuzzIEEE/PizzileoL07 fatcat:r7qiasboavgpjfjf7gfpupxfsy
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