Relevant bibliographies by topics / Fuzzy Support Vector Machine (FSVM)

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  2. Dissertations / Theses
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Academic literature on the topic 'Fuzzy Support Vector Machine (FSVM)'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Fuzzy Support Vector Machine (FSVM)"

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Zhang, Rui, Tong Bo Liu, and Ming Wen Zheng. "An New Fuzzy Support Vector Machine for Binary Classification." Advanced Materials Research 433-440 (January 2012): 2856–61. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2856.

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In this paper, we proposed a new fuzzy support vector machine(called L2–FSVM here), which error part of object is L2–norm.Meanwhile we introduce a new method of generating fuzzy memberships so as to reduce to effects of outliers. The experimental results demonstrate that the L2-FSVM method provides improved ability to reduce to effects of outliers in comparison with traditional SVMs and FSVMs, and claim that L2–FSVM is the best way to solve the binary classification in the three methods stated above.
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Zhou, Huaping, and Huangli Qin. "Self-Adjusting Fuzzy Support Vector Machine Based on Analysis of Potential Support Vector Sample Point." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 10 (September 2019): 1959035. http://dx.doi.org/10.1142/s0218001419590353.

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Fuzzy support vector machine (FSVM) is a part of machine learning with its good classification effect. So far, there are two most commonly used FSVM models: FSVM on account of class core and fuzzy support vector machine on account of hyperplane that is over class core. Each has its own problems: FSVM on account of class core are dependent on the geometric shape of sample sets. Although FSVM on account of hyperplane that is over class core can solve the above problems to some extent. However, this algorithm has low generalization ability and high time complexity. Therefore, Inspired by these two common models, the paper proposes an improved membership function method. By analyzing and calculating the potential support vector sample points, adjustment factor is obtained, which drives the class core to adjust along the direction away from the outliers. In this way, membership of noise and outliers are reduced and the membership of support vector will also increase to some extent. In this paper, a new experimental comparison method is used, which can make the comparison of classification effect more obvious and convincing. The experimental part compares the proposed FSVM model with the above two FSVM models. It shows that the proposed algorithm improves the stability and classification accuracy to some extent.
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Shanmugapriya, P., and Y. Venkataramani. "Analysis of Speaker Verification System Using Support Vector Machine." JOURNAL OF ADVANCES IN CHEMISTRY 13, no. 10 (February 25, 2017): 6531–42. http://dx.doi.org/10.24297/jac.v13i10.5839.

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The integration of GMM- super vector and Support Vector Machine (SVM) has become one of most popular strategy in text-independent speaker verification system. This paper describes the application of Fuzzy Support Vector Machine (FSVM) for classification of speakers using GMM-super vectors. Super vectors are formed by stacking the mean vectors of adapted GMMs from UBM using maximum a posteriori (MAP). GMM super vectors characterize speaker’s acoustic characteristics which are used for developing a speaker dependent fuzzy SVM model. Introducing fuzzy theory in support vector machine yields better classification accuracy and requires less number of support vectors. Experiments were conducted on 2001 NIST speaker recognition evaluation corpus. Performance of GMM-FSVM based speaker verification system is compared with the conventional GMM-UBM and GMM-SVM based systems. Experimental results indicate that the fuzzy SVM based speaker verification system with GMM super vector achieves better performance to GMM-UBM system. Â
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Ke, Hong Xia, Guo Dong Liu, and Guo Bing Pan. "Fuzzy Support Vector Machine for PolSAR Image Classification." Advanced Materials Research 639-640 (January 2013): 1162–67. http://dx.doi.org/10.4028/www.scientific.net/amr.639-640.1162.

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Fully Polarimetric Synthetic Aperture Radar (PolSAR) image classification, with the complexity for its data’s scattering mechanism and statistical property, has expected to be performed by an automatic categorization. This paper presents a supervised method called Fuzzy support vector machine (FSVM), which is a variant of the SVM algorithm to classify the PolSAR image data. In order to take advantages of PolSAR data, five scattering features (entropy, total power, three Eigenvalues of Coherent Matrix: λ1,λ2,λ3) are input as original data space of the FSVM algorithm. The feasibility of this approach is examined by the JPL/AIRSAR PolSAR data. The classification results show that the proposed FSVM method has out-performed the SVM method.
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Duan, Hua, and Yan Mei Hou. "A Solving Algorithm of Fuzzy Support Vector Machines Based on Determination of Membership." Advanced Materials Research 756-759 (September 2013): 3399–403. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3399.

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In order to overcome the issues that Support Vector Machine is sensitive to the outlier and noise points, Fuzzy Support Vector Machine (FSVM) is proposed. The key issue to solve the FSVM is determinate the fuzzy membership. This paper gives an overview of construction algorithm of the fuzzy membership. We also give an algorithm to solve FSVM that is derived from improved-SMO algorithm.
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Yu, Lean. "Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier." Discrete Dynamics in Nature and Society 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/564213.

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A least squares fuzzy support vector machine (LS-FSVM) model that integrates advantages of fuzzy support vector machine (FSVM) and least squares method is proposed for credit risk evaluation. In the proposed LS-FSVM model, the purpose of incorporating the concepts of fuzzy sets is to add generalization capability and outlier insensitivity, while the least squares method is adopted to reduce the computational complexity. For illustrative purposes, a real-world credit risk dataset is used to test the effectiveness and robustness of the proposed LS-FSVM methodology.
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Hou, Yuan Bin, Ning Li, Fan Guo, and Jing Chen. "Fault Diagnosis of Conveyance Machine Based on Fuzzy Support Vector." Applied Mechanics and Materials 135-136 (October 2011): 547–52. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.547.

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Aiming random and nonlinearity for conveyance machine of rubber belt in mine, a method of fault diagnosis is presented which fusion of fuzzy theory and support vector machine (FSVM). According to the coal mine safety rules of the regulation, the conveyance machine servicing are deduced eleven faults after analyzing practice statistic data, here, we consider some are fuzzy that the statistic data are divided to the normal kind or fault kind, but some are definite that the statistic data possibility are belong to same kind fault, accordingly, the fuzzy support vectors is established. Farther, two kernel functions of FSVM is made for seeking the problem of random and nonlinearity, which are RBF and TANH. According to the random statistic data and the study sample, analyzing the effect of expense and kernel function in selecting different parameters, the unitary constant is ascertained, next, the FSVM kernel function of fault diagnosis multi-class rules are established, then, this method availability is proved using test data and simulation.
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Li, Kai, and Xiao Xia Lu. "A Rough Margin Based Fuzzy Support Vector Machine." Advanced Materials Research 204-210 (February 2011): 879–82. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.879.

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By combining fuzzy support vector machine with rough set, we propose a rough margin based fuzzy support vector machine (RFSVM). It inherits the characteristic of the FSVM method and considers position of training samples of the rough margin in order to reduce overfitting due to noises or outliers. The new proposed algorithm finds the optimal separating hyperplane that maximizes the rough margin containing lower margin and upper margin. Meanwhile, the points lied on the lower margin have larger penalty than these in the boundary of the rough margin. Experiments on several benchmark datasets show that the RFSVM algorithm is effective and feasible compared with the existing support vector machines.
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Gu, Xiaoqing, Tongguang Ni, and Hongyuan Wang. "New Fuzzy Support Vector Machine for the Class Imbalance Problem in Medical Datasets Classification." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/536434.

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In medical datasets classification, support vector machine (SVM) is considered to be one of the most successful methods. However, most of the real-world medical datasets usually contain some outliers/noise and data often have class imbalance problems. In this paper, a fuzzy support machine (FSVM) for the class imbalance problem (called FSVM-CIP) is presented, which can be seen as a modified class of FSVM by extending manifold regularization and assigning two misclassification costs for two classes. The proposed FSVM-CIP can be used to handle the class imbalance problem in the presence of outliers/noise, and enhance the locality maximum margin. Five real-world medical datasets, breast, heart, hepatitis, BUPA liver, and pima diabetes, from the UCI medical database are employed to illustrate the method presented in this paper. Experimental results on these datasets show the outperformed or comparable effectiveness of FSVM-CIP.
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Chen, Hao Guang, Xiao Xi Li, and Da Xi Li. "New Fuzzy Support Vector Machine Method Based on Entropy and Ant-Colony Optimization." Applied Mechanics and Materials 380-384 (August 2013): 1580–84. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1580.

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Concerning the defect of fuzzy membership as a function of distance between the point and its class center in feature space for some current Fuzzy Support Vector Machines (FSVM), a new FSVM based on entropy and Genetic Algorithm (GA) named EGFSVM was proposed in this paper. Making use of evaluation of entropy and intelligence of GA, EGFSVM enhances the classification capability and makes clustering center more suitable and membership more accurate. Experimental results show EGFSVM has better precision and classification performance, especially to multi-class and large scale data.
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Dissertations / Theses on the topic "Fuzzy Support Vector Machine (FSVM)"

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Kannan, Anand. "Performance evaluation of security mechanisms in Cloud Networks." Thesis, KTH, Kommunikationssystem, CoS, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-99464.

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Infrastructure as a Service (IaaS) is a cloud service provisioning model which largely focuses on data centre provisioning of computing and storage facilities. The networking aspects of IaaS beyond the data centre are a limiting factor preventing communication services that are sensitive to network characteristics from adopting this approach. Cloud networking is a new technology which integrates network provisioning with the existing cloud service provisioning models thereby completing the cloud computing picture by addressing the networking aspects. In cloud networking, shared network resources are virtualized, and provisioned to customers and end-users on-demand in an elastic fashion. This technology allows various kinds of optimization, e.g., reducing latency and network load. Further, this allows service providers to provision network performance guarantees as a part of their service offering. However, this new approach introduces new security challenges. Many of these security challenges are addressed in the CloNe security architecture. This thesis presents a set of potential techniques for securing different resource in a cloud network environment which are not addressed in the existing CloNe security architecture. The thesis begins with a holistic view of the Cloud networking, as described in the Scalable and Adaptive Internet Solutions (SAIL) project, along with its proposed architecture and security goals. This is followed by an overview of the problems that need to be solved and some of the different methods that can be applied to solve parts of the overall problem, specifically a comprehensive, tightly integrated, and multi-level security architecture, a key management algorithm to support the access control mechanism, and an intrusion detection mechanism. For each method or set of methods, the respective state of the art is presented. Additionally, experiments to understand the performance of these mechanisms are evaluated on a simple cloud network test bed. The proposed key management scheme uses a hierarchical key management approach that provides fast and secure key update when member join and member leave operations are carried out. Experiments show that the proposed key management scheme enhances the security and increases the availability and integrity. A newly proposed genetic algorithm based feature selection technique has been employed for effective feature selection. Fuzzy SVM has been used on the data set for effective classification. Experiments have shown that the proposed genetic based feature selection algorithm reduces the number of features and hence decreases the classification time, while improving detection accuracy of the fuzzy SVM classifier by minimizing the conflicting rules that may confuse the classifier. The main advantages of this intrusion detection system are the reduction in false positives and increased security.
Infrastructure as a Service (IaaS) är en Cloudtjänstmodell som huvudsakligen är inriktat på att tillhandahålla ett datacenter för behandling och lagring av data. Nätverksaspekterna av en cloudbaserad infrastruktur som en tjänst utanför datacentret utgör en begränsande faktor som förhindrar känsliga kommunikationstjänster från att anamma denna teknik. Cloudnätverk är en ny teknik som integrerar nätverkstillgång med befintliga cloudtjänstmodeller och därmed fullbordar föreställningen av cloud data genom att ta itu med nätverkaspekten.  I cloudnätverk virtualiseras delade nätverksresurser, de avsätts till kunder och slutanvändare vid efterfrågan på ett flexibelt sätt. Denna teknik tillåter olika typer av möjligheter, t.ex. att minska latens och belastningen på nätet. Vidare ger detta tjänsteleverantörer ett sätt att tillhandahålla garantier för nätverksprestandan som en del av deras tjänsteutbud. Men denna nya strategi introducerar nya säkerhetsutmaningar, exempelvis VM migration genom offentligt nätverk. Många av dessa säkerhetsutmaningar behandlas i CloNe’s Security Architecture. Denna rapport presenterar en rad av potentiella tekniker för att säkra olika resurser i en cloudbaserad nätverksmiljö som inte behandlas i den redan existerande CloNe Security Architecture. Rapporten inleds med en helhetssyn på cloudbaserad nätverk som beskrivs i Scalable and Adaptive Internet Solutions (SAIL)-projektet, tillsammans med dess föreslagna arkitektur och säkerhetsmål. Detta följs av en översikt över de problem som måste lösas och några av de olika metoder som kan tillämpas för att lösa delar av det övergripande problemet. Speciellt behandlas en omfattande och tätt integrerad multi-säkerhetsarkitektur, en nyckelhanteringsalgoritm som stödjer mekanismens åtkomstkontroll och en mekanism för intrångsdetektering. För varje metod eller för varje uppsättning av metoder, presenteras ståndpunkten för respektive teknik. Dessutom har experimenten för att förstå prestandan av dessa mekanismer utvärderats på testbädd av ett enkelt cloudnätverk. Den föreslagna nyckelhantering system använder en hierarkisk nyckelhantering strategi som ger snabb och säker viktig uppdatering när medlemmar ansluta sig till och medlemmarna lämnar utförs. Försöksresultat visar att den föreslagna nyckelhantering system ökar säkerheten och ökar tillgänglighet och integritet. En nyligen föreslagna genetisk algoritm baserad funktion valet teknik har använts för effektiv funktion val. Fuzzy SVM har använts på de uppgifter som för effektiv klassificering. Försök har visat att den föreslagna genetiska baserad funktion selekteringsalgoritmen minskar antalet funktioner och därmed minskar klassificering tiden, och samtidigt förbättra upptäckt noggrannhet fuzzy SVM klassificeraren genom att minimera de motstående regler som kan förvirra klassificeraren. De främsta fördelarna med detta intrångsdetekteringssystem är den minskning av falska positiva och ökad säkerhet.
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Benbrahim, Houda. "A fuzzy semi-supervised support vector machine approach to hypertext categorization." Thesis, University of Portsmouth, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.494145.

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As the web expands exponentially, the need to put some order to its content becomes apparent. Hypertext categorization, that is the automatic classification of web documents into predefined classes, came to elevate humans from that task. The extra information available in a hypertext document poses new challenges for automatic categorization. HTML tags and linked neighbourhood all provide rich information for hypertext categorization that is no available in traditional text classification.
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Uslan, Volkan. "Support vector machine-based fuzzy systems for quantitative prediction of peptide binding affinity." Thesis, De Montfort University, 2015. http://hdl.handle.net/2086/11170.

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Reliable prediction of binding affinity of peptides is one of the most challenging but important complex modelling problems in the post-genome era due to the diversity and functionality of the peptides discovered. Generally, peptide binding prediction models are commonly used to find out whether a binding exists between a certain peptide(s) and a major histocompatibility complex (MHC) molecule(s). Recent research efforts have been focused on quantifying the binding predictions. The objective of this thesis is to develop reliable real-value predictive models through the use of fuzzy systems. A non-linear system is proposed with the aid of support vector-based regression to improve the fuzzy system and applied to the real value prediction of degree of peptide binding. This research study introduced two novel methods to improve structure and parameter identification of fuzzy systems. First, the support-vector based regression is used to identify initial parameter values of the consequent part of type-1 and interval type-2 fuzzy systems. Second, an overlapping clustering concept is used to derive interval valued parameters of the premise part of the type-2 fuzzy system. Publicly available peptide binding affinity data sets obtained from the literature are used in the experimental studies of this thesis. First, the proposed models are blind validated using the peptide binding affinity data sets obtained from a modelling competition. In that competition, almost an equal number of peptide sequences in the training and testing data sets (89, 76, 133 and 133 peptides for the training and 88, 76, 133 and 47 peptides for the testing) are provided to the participants. Each peptide in the data sets was represented by 643 bio-chemical descriptors assigned to each amino acid. Second, the proposed models are cross validated using mouse class I MHC alleles (H2-Db, H2-Kb and H2-Kk). H2-Db, H2-Kb, and H2-Kk consist of 65 nona-peptides, 62 octa-peptides, and 154 octa-peptides, respectively. Compared to the previously published results in the literature, the support vector-based type-1 and support vector-based interval type-2 fuzzy models yield an improvement in the prediction accuracy. The quantitative predictive performances have been improved as much as 33.6\% for the first group of data sets and 1.32\% for the second group of data sets. The proposed models not only improved the performance of the fuzzy system (which used support vector-based regression), but the support vector-based regression benefited from the fuzzy concept also. The results obtained here sets the platform for the presented models to be considered for other application domains in computational and/or systems biology. Apart from improving the prediction accuracy, this research study has also identified specific features which play a key role(s) in making reliable peptide binding affinity predictions. The amino acid features "Polarity", "Positive charge", "Hydrophobicity coefficient", and "Zimm-Bragg parameter" are considered as highly discriminating features in the peptide binding affinity data sets. This information can be valuable in the design of peptides with strong binding affinity to a MHC I molecule(s). This information may also be useful when designing drugs and vaccines.
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OLIVEIRA, A. B. "Modelo de Predição para análise comparativa de Técnicas Neuro-Fuzzy e de Regressão." Universidade Federal do Espírito Santo, 2010. http://repositorio.ufes.br/handle/10/4218.

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Made available in DSpace on 2016-08-29T15:33:12Z (GMT). No. of bitstreams: 1 tese_3521_.pdf: 2782962 bytes, checksum: d4b2294e5ee9ab86b7a35aec083af692 (MD5) Previous issue date: 2010-02-12
Os Modelos de Predição implementados pelos algoritmos de Aprendizagem de Máquina advindos como linha de pesquisa da Inteligência Computacional são resultantes de pesquisas e investigações empíricas em dados do mundo real. Neste contexto; estes modelos são extraídos para comparação de duas grandes técnicas de aprendizagem de máquina Redes Neuro-Fuzzy e de Regressão aplicadas no intuito de estimar um parâmetro de qualidade do produto em um ambiente industrial sob processo contínuo. Heuristicamente; esses Modelos de Predição são aplicados e comparados em um mesmo ambiente de simulação com intuito de mensurar os níveis de adequação dos mesmos, o poder de desempenho e generalização dos dados empíricos que compõem este cenário (ambiente industrial de mineração).
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Abo, Al Ahad George, and Abbas Salami. "Machine Learning for Market Prediction : Soft Margin Classifiers for Predicting the Sign of Return on Financial Assets." Thesis, Linköpings universitet, Produktionsekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151459.

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Forecasting procedures have found applications in a wide variety of areas within finance and have further shown to be one of the most challenging areas of finance. Having an immense variety of economic data, stakeholders aim to understand the current and future state of the market. Since it is hard for a human to make sense out of large amounts of data, different modeling techniques have been applied to extract useful information from financial databases, where machine learning techniques are among the most recent modeling techniques. Binary classifiers such as Support Vector Machines (SVMs) have to some extent been used for this purpose where extensions of the algorithm have been developed with increased prediction performance as the main goal. The objective of this study has been to develop a process for improving the performance when predicting the sign of return of financial time series with soft margin classifiers. An analysis regarding the algorithms is presented in this study followed by a description of the methodology that has been utilized. The developed process containing some of the presented soft margin classifiers, and other aspects of kernel methods such as Multiple Kernel Learning have shown pleasant results over the long term, in which the capability of capturing different market conditions have been shown to improve with the incorporation of different models and kernels, instead of only a single one. However, the results are mostly congruent with earlier studies in this field. Furthermore, two research questions have been answered where the complexity regarding the kernel functions that are used by the SVM have been studied and the robustness of the process as a whole. Complexity refers to achieving more complex feature maps through combining kernels by either adding, multiplying or functionally transforming them. It is not concluded that an increased complexity leads to a consistent improvement, however, the combined kernel function is superior during some of the periods of the time series used in this thesis for the individual models. The robustness has been investigated for different signal-to-noise ratio where it has been observed that windows with previously poor performance are more exposed to noise impact.
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Chida, Anjum A. "Protein Tertiary Model Assessment Using Granular Machine Learning Techniques." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/cs_diss/65.

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The automatic prediction of protein three dimensional structures from its amino acid sequence has become one of the most important and researched fields in bioinformatics. As models are not experimental structures determined with known accuracy but rather with prediction it’s vital to determine estimates of models quality. We attempt to solve this problem using machine learning techniques and information from both the sequence and structure of the protein. The goal is to generate a machine that understands structures from PDB and when given a new model, predicts whether it belongs to the same class as the PDB structures (correct or incorrect protein models). Different subsets of PDB (protein data bank) are considered for evaluating the prediction potential of the machine learning methods. Here we show two such machines, one using SVM (support vector machines) and another using fuzzy decision trees (FDT). First using a preliminary encoding style SVM could get around 70% in protein model quality assessment accuracy, and improved Fuzzy Decision Tree (IFDT) could reach above 80% accuracy. For the purpose of reducing computational overhead multiprocessor environment and basic feature selection method is used in machine learning algorithm using SVM. Next an enhanced scheme is introduced using new encoding style. In the new style, information like amino acid substitution matrix, polarity, secondary structure information and relative distance between alpha carbon atoms etc is collected through spatial traversing of the 3D structure to form training vectors. This guarantees that the properties of alpha carbon atoms that are close together in 3D space and thus interacting are used in vector formation. With the use of fuzzy decision tree, we obtained a training accuracy around 90%. There is significant improvement compared to previous encoding technique in prediction accuracy and execution time. This outcome motivates to continue to explore effective machine learning algorithms for accurate protein model quality assessment. Finally these machines are tested using CASP8 and CASP9 templates and compared with other CASP competitors, with promising results. We further discuss the importance of model quality assessment and other information from proteins that could be considered for the same.
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Thomas, Rodney H. "Machine Learning for Exploring State Space Structure in Genetic Regulatory Networks." Diss., NSUWorks, 2018. https://nsuworks.nova.edu/gscis_etd/1053.

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Genetic regulatory networks (GRN) offer a useful model for clinical biology. Specifically, such networks capture interactions among genes, proteins, and other metabolic factors. Unfortunately, it is difficult to understand and predict the behavior of networks that are of realistic size and complexity. In this dissertation, behavior refers to the trajectory of a state, through a series of state transitions over time, to an attractor in the network. This project assumes asynchronous Boolean networks, implying that a state may transition to more than one attractor. The goal of this project is to efficiently identify a network's set of attractors and to predict the likelihood with which an arbitrary state leads to each of the network’s attractors. These probabilities will be represented using a fuzzy membership vector. Predicting fuzzy membership vectors using machine learning techniques may address the intractability posed by networks of realistic size and complexity. Modeling and simulation can be used to provide the necessary training sets for machine learning methods to predict fuzzy membership vectors. The experiments comprise several GRNs, each represented by a set of output classes. These classes consist of thresholds τ and ¬τ, where τ = [τlaw,τhigh]; state s belongs to class τ if the probability of its transitioning to attractor 􀜣 belongs to the range [τlaw,τhigh]; otherwise it belongs to class ¬τ. Finally, each machine learning classifier was trained with the training sets that was previously collected. The objective is to explore methods to discover patterns for meaningful classification of states in realistically complex regulatory networks. The research design took a GRN and a machine learning method as input and produced output class < Ατ > and its negation ¬ < Ατ >. For each GRN, attractors were identified, data was collected by sampling each state to create fuzzy membership vectors, and machine learning methods were trained to predict whether a state is in a healthy attractor or not. For T-LGL, SVMs had the highest accuracy in predictions (between 93.6% and 96.9%) and precision (between 94.59% and 97.87%). However, naive Bayesian classifiers had the highest recall (between 94.71% and 97.78%). This study showed that all experiments have extreme significance with pvalue < 0.0001. The contribution this research offers helps clinical biologist to submit genetic states to get an initial result on their outcomes. For future work, this implementation could use other machine learning classifiers such as xgboost or deep learning methods. Other suggestions offered are developing methods that improves the performance of state transition that allow for larger training sets to be sampled.
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Díaz, Jorge Luis Guevara. "Modelos de aprendizado supervisionado usando métodos kernel, conjuntos fuzzy e medidas de probabilidade." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-03122015-155546/.

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Esta tese propõe uma metodologia baseada em métodos de kernel, teoria fuzzy e probabilidade para tratar conjuntos de dados cujas observações são conjuntos de pontos. As medidas de probabilidade e os conjuntos fuzzy são usados para modelar essas observações. Posteriormente, graças a kernels definidos sobre medidas de probabilidade, ou em conjuntos fuzzy, é feito o mapeamento implícito dessas medidas de probabilidade, ou desses conjuntos fuzzy, para espaços de Hilbert com kernel reproduzível, onde a análise pode ser feita com algum método kernel. Usando essa metodologia, é possível fazer frente a uma ampla gamma de problemas de aprendizado para esses conjuntos de dados. Em particular, a tese apresenta o projeto de modelos de descrição de dados para observações modeladas com medidas de probabilidade. Isso é conseguido graças ao mergulho das medidas de probabilidade nos espaços de Hilbert, e a construção de esferas envolventes mínimas nesses espaços de Hilbert. A tese apresenta como esses modelos podem ser usados como classificadores de uma classe, aplicados na tarefa de detecção de anomalias grupais. No caso que as observações sejam modeladas por conjuntos fuzzy, a tese propõe mapear esses conjuntos fuzzy para os espaços de Hilbert com kernel reproduzível. Isso pode ser feito graças à projeção de novos kernels definidos sobre conjuntos fuzzy. A tese apresenta como esses novos kernels podem ser usados em diversos problemas como classificação, regressão e na definição de distâncias entre conjuntos fuzzy. Em particular, a tese apresenta a aplicação desses kernels em problemas de classificação supervisionada em dados intervalares e teste kernel de duas amostras para dados contendo atributos imprecisos.
This thesis proposes a methodology based on kernel methods, probability measures and fuzzy sets, to analyze datasets whose individual observations are itself sets of points, instead of individual points. Fuzzy sets and probability measures are used to model observations; and kernel methods to analyze the data. Fuzzy sets are used when the observation contain imprecise, vague or linguistic values. Whereas probability measures are used when the observation is given as a set of multidimensional points in a $D$-dimensional Euclidean space. Using this methodology, it is possible to address a wide range of machine learning problems for such datasets. Particularly, this work presents data description models when observations are modeled by probability measures. Those description models are applied to the group anomaly detection task. This work also proposes a new class of kernels, \\emph{the kernels on fuzzy sets}, that are reproducing kernels able to map fuzzy sets to a geometric feature spaces. Those kernels are similarity measures between fuzzy sets. We give from basic definitions to applications of those kernels in machine learning problems as supervised classification and a kernel two-sample test. Potential applications of those kernels include machine learning and patter recognition tasks over fuzzy data; and computational tasks requiring a similarity measure estimation between fuzzy sets.
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Hu, Linlin. "A novel hybrid technique for short-term electricity price forecasting in deregulated electricity markets." Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/4498.

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Short-term electricity price forecasting is now crucial practice in deregulated electricity markets, as it forms the basis for maximizing the profits of the market participants. In this thesis, short-term electricity prices are forecast using three different predictor schemes, Artificial Neural Networks (ANNs), Support Vector Machine (SVM) and a hybrid scheme, respectively. ANNs are the very popular and successful tools for practical forecasting. In this thesis, a hidden-layered feed-forward neural network with back-propagation has been adopted for detailed comparison with other forecasting models. SVM is a newly developed technique that has many attractive features and good performance in terms of prediction. In order to overcome the limitations of individual forecasting models, a hybrid technique that combines Fuzzy-C-Means (FCM) clustering and SVM regression algorithms is proposed to forecast the half-hour electricity prices in the UK electricity markets. According to the value of their power prices, thousands of the training data are classified by the unsupervised learning method of FCM clustering. SVM regression model is then applied to each cluster by taking advantage of the aggregated data information, which reduces the noise for each training program. In order to demonstrate the predictive capability of the proposed model, ANNs and SVM models are presented and compared with the hybrid technique based on the same training and testing data sets in the case studies by using real electricity market data. The data was obtained upon request from APX Power UK for the year 2007. Mean Absolute Percentage Error (MAPE) is used to analyze the forecasting errors of different models and the results presented clearly show that the proposed hybrid technique considerably improves the electricity price forecasting.
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Chen, Xiujuan. "Computational Intelligence Based Classifier Fusion Models for Biomedical Classification Applications." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/26.

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The generalization abilities of machine learning algorithms often depend on the algorithms’ initialization, parameter settings, training sets, or feature selections. For instance, SVM classifier performance largely relies on whether the selected kernel functions are suitable for real application data. To enhance the performance of individual classifiers, this dissertation proposes classifier fusion models using computational intelligence knowledge to combine different classifiers. The first fusion model called T1FFSVM combines multiple SVM classifiers through constructing a fuzzy logic system. T1FFSVM can be improved by tuning the fuzzy membership functions of linguistic variables using genetic algorithms. The improved model is called GFFSVM. To better handle uncertainties existing in fuzzy MFs and in classification data, T1FFSVM can also be improved by applying type-2 fuzzy logic to construct a type-2 fuzzy classifier fusion model (T2FFSVM). T1FFSVM, GFFSVM, and T2FFSVM use accuracy as a classifier performance measure. AUC (the area under an ROC curve) is proved to be a better classifier performance metric. As a comparison study, AUC-based classifier fusion models are also proposed in the dissertation. The experiments on biomedical datasets demonstrate promising performance of the proposed classifier fusion models comparing with the individual composing classifiers. The proposed classifier fusion models also demonstrate better performance than many existing classifier fusion methods. The dissertation also studies one interesting phenomena in biology domain using machine learning and classifier fusion methods. That is, how protein structures and sequences are related each other. The experiments show that protein segments with similar structures also share similar sequences, which add new insights into the existing knowledge on the relation between protein sequences and structures: similar sequences share high structure similarity, but similar structures may not share high sequence similarity.
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Books on the topic "Fuzzy Support Vector Machine (FSVM)"

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The nonlinear workbook: Chaos, fractals, cellular automata, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, fuzzy logic with C++, Java and symbolic C++ programs. 6th ed. Hackensack, New Jersey: World Scientific, 2015.

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The nonlinear workbook: Chaos, fractals, cellular automata, neural networks, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, Fuzzy logic with C++, Java and SymbolicC++ programs. 5th ed. New Jersey: World Scientific, 2011.

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The nonlinear workbook: Chaos, fractals, cellular automata, neural networks, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, Fuzzy logic with C++, Java and SymbolicC++ programs. 3rd ed. Hackensack, NJ: World Scientific, 2005.

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The nonlinear workbook: Chaos, fractals, cellular automata, neural networks, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, Fuzzy logic with C++, Java and SymbolicC++ programs. 5th ed. New Jersey: World Scientific, 2011.

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The nonlinear workbook: Chaos, fractals, cellular automata, neural networks, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, Fuzzy logic with C++, Java and SymbolicC++ programs. 4th ed. New Jersey: World Scientific, 2008.

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Computational Intelligence and Its Applications: Evolutionary Computation, Fuzzy Logic, Neural Network and Support Vector Machine Techniques. Imperial College Press, 2012.

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Book chapters on the topic "Fuzzy Support Vector Machine (FSVM)"

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Zhang, Hao, Kang Li, and Cheng Wu. "Fuzzy Chance Constrained Support Vector Machine." In Lecture Notes in Computer Science, 270–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15621-2_30.

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Benbrahim, Houda. "Fuzzy Semi-supervised Support Vector Machines." In Machine Learning and Data Mining in Pattern Recognition, 127–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23199-5_10.

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Ganji, Hamed, and Shahram Khadivi. "ASVMFC: Adaptive Support Vector Machine Based Fuzzy Classifier." In Information Retrieval Technology, 340–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25631-8_31.

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Hao, Pei-Yi. "A New Fuzzy Support Vector Data Description Machine." In Modern Advances in Applied Intelligence, 118–27. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07455-9_13.

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Wu, Xiao, Yan Wei, and Xia Wu. "Improved Double Memberships of Fuzzy Support Vector Machine." In Lecture Notes in Electrical Engineering, 764–71. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2386-6_99.

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Yang, Tao, and Bo Hu. "Study of Multiuser Detection: The Support Vector Machine Approach." In Fuzzy Systems and Knowledge Discovery, 442–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11540007_54.

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Hwang, Changha, Dug Hun Hong, Eunyoung Na, Hyejung Park, and Jooyong Shim. "Interval Regression Analysis Using Support Vector Machine and Quantile Regression." In Fuzzy Systems and Knowledge Discovery, 100–109. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539506_12.

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Choi, YoungSik, and KiJoo Kim. "Video Summarization Using Fuzzy One-Class Support Vector Machine." In Computational Science and Its Applications – ICCSA 2004, 49–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24707-4_7.

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Wu, Kui, and Kim-Hui Yap. "Soft-Labeling Image Scheme Using Fuzzy Support Vector Machine." In Studies in Computational Intelligence, 271–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-76827-2_11.

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Hao, Yanyou, Zhongxian Chi, Deqin Yan, and Xun Yue. "An Improved Fuzzy Support Vector Machine for Credit Rating." In Lecture Notes in Computer Science, 495–505. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74784-0_50.

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Conference papers on the topic "Fuzzy Support Vector Machine (FSVM)"

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Lakshmanan, B., A. Jeril Priscilla, S. Ponni, and V. Sankari. "Evaluation of imbalanced datasets using fuzzy support vector machine-class imbalance learning (FSVM-CIL)." In 2011 International Conference on Recent Trends in Information Technology (ICRTIT). IEEE, 2011. http://dx.doi.org/10.1109/icrtit.2011.5972431.

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Xiao, Xiaoling, and Xiang Zhang. "An Improved Fuzzy Support Vector Machine." In 2009 International Symposium on Intelligent Ubiquitous Computing and Education, IUCE. IEEE, 2009. http://dx.doi.org/10.1109/iuce.2009.101.

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Shilton, Alistair, and Daniel T. H. Lai. "Iterative Fuzzy Support Vector Machine Classification." In 2007 IEEE International Fuzzy Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/fuzzy.2007.4295570.

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Wu, Qing, and Kaiyue Sun. "An improved fuzzy twin support vector machine based on support vector." In 2017 13th IEEE Conference on Automation Science and Engineering (CASE 2017). IEEE, 2017. http://dx.doi.org/10.1109/coase.2017.8256256.

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Xue, Zhenxia, and Wanli Liu. "A fuzzy rough support vector regression machine." In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2012. http://dx.doi.org/10.1109/fskd.2012.6234232.

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Cheng, Gong, and Xiaojun Tong. "Fuzzy Clustering Multiple Kernel Support Vector Machine." In 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2018. http://dx.doi.org/10.1109/icwapr.2018.8521307.

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Li, Xuehua, and Lan Shu. "Fuzzy Theory Based Support Vector Machine Classifier." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.440.

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Sevakula, Rahul K., and Nishchal K. Verma. "Fuzzy Support Vector Machine using Hausdorff distance." In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2013. http://dx.doi.org/10.1109/fuzz-ieee.2013.6622475.

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Wang, Xi-zhao, and Shu-xia Lu. "Improved Fuzzy Multicategory Support Vector Machines Classifier." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258575.

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Hao, Pei-Yi. "Possibilistic regression analysis by support vector machine." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007433.

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