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dc.contributor.advisorSnášel, Václav
dc.contributor.authorOjha, Varun Kumar
dc.date.accessioned2016-11-01T10:03:43Z
dc.date.available2016-11-01T10:03:43Z
dc.date.issued2016
dc.identifier.otherOSD002cs
dc.identifier.urihttp://hdl.handle.net/10084/112274
dc.descriptionImport 02/11/2016cs
dc.description.abstractMultiobjective heterogeneous flexible neural tree (HFNT) and multiobjective hierarchical fuzzy inference tree (HFIT) are two novel adaptive algorithms, which were proposed for the feature selection and function approximation after comprehensive literature reviews of the neural network and fuzzy inference system paradigms, respectively. The proposed algorithms were designed as a tree-like model, and the best tree-structure was selected from a topological space by applying a multiobjective evolutionary algorithm that simultaneously minimized both approximation error and tree complexity. Further, the parameter vector of the selected tree, from the Pareto front, was tuned by using a metaheuristic algorithm. For HFNT, the dynamics of natural selection was exploited to introduce functional heterogeneity in the HFNT nodes, and a diversity index was introduced for creating diverse HFNTs during its tree optimization phase. Subsequently, an evolutionary ensemble of HFNTs was proposed for making use of the final population. On the other hand, the HFIT nodes were low-dimensional type-1 or type-2 fuzzy inference systems, and the tree-like model was a hierarchical arrangement of such nodes. The performance of both HFNT and HFIT on benchmark datasets was better than the performance of the algorithms in the literature. Additionally, both HFNT and HFIT was used for the predictive modeling of the industrial problems, in which the feature selection was a crucial challenge in addition to the prediction. High approximation ability with the simple model generation is the vital contribution of the proposed algorithms for predictive modeling of complex problems.en
dc.description.abstractMultiobjective heterogeneous flexible neural tree (HFNT) and multiobjective hierarchical fuzzy inference tree (HFIT) are two novel adaptive algorithms, which were proposed for the feature selection and function approximation after comprehensive literature reviews of the neural network and fuzzy inference system paradigms, respectively. The proposed algorithms were designed as a tree-like model, and the best tree-structure was selected from a topological space by applying a multiobjective evolutionary algorithm that simultaneously minimized both approximation error and tree complexity. Further, the parameter vector of the selected tree, from the Pareto front, was tuned by using a metaheuristic algorithm. For HFNT, the dynamics of natural selection was exploited to introduce functional heterogeneity in the HFNT nodes, and a diversity index was introduced for creating diverse HFNTs during its tree optimization phase. Subsequently, an evolutionary ensemble of HFNTs was proposed for making use of the final population. On the other hand, the HFIT nodes were low-dimensional type-1 or type-2 fuzzy inference systems, and the tree-like model was a hierarchical arrangement of such nodes. The performance of both HFNT and HFIT on benchmark datasets was better than the performance of the algorithms in the literature. Additionally, both HFNT and HFIT was used for the predictive modeling of the industrial problems, in which the feature selection was a crucial challenge in addition to the prediction. High approximation ability with the simple model generation is the vital contribution of the proposed algorithms for predictive modeling of complex problems.cs
dc.format160 s. : il.cs
dc.format.extent6798956 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherVysoká škola báňská - Technická univerzita Ostravacs
dc.subjectfeedforward neural networken
dc.subjectfuzzy inference systemen
dc.subjectmultiobjectiveen
dc.subjectmetaheuristicsen
dc.subjectensemble learningen
dc.subjectfeature selectionen
dc.subjectfeedforward neural networkcs
dc.subjectfuzzy inference systemcs
dc.subjectmultiobjectivecs
dc.subjectmetaheuristicscs
dc.subjectensemble learningcs
dc.subjectfeature selectioncs
dc.titleFeature Selection and Function Approximation Using Adaptive Algorithmsen
dc.title.alternativeVýběr atributů a aproximace funkcí využívající adaptivní algoritmycs
dc.typeDisertační prácecs
dc.identifier.signature201600189cs
dc.identifier.locationÚK/Studovna
dc.contributor.refereeDahal, Keshavcs
dc.contributor.refereeSekanina, Lukášcs
dc.contributor.refereePalade, Vasilecs
dc.date.accepted2016-09-26
dc.thesis.degree-namePh.D.
dc.thesis.degree-levelDoktorský studijní programcs
dc.thesis.degree-grantorVysoká škola báňská - Technická univerzita Ostrava. Fakulta elektrotechniky a informatikycs
dc.description.department460 - Katedra informatiky
dc.thesis.degree-programInformatika, komunikační technologie a aplikovaná matematikacs
dc.thesis.degree-branchInformatikacs
dc.description.resultvyhovělcs
dc.identifier.senderS2724cs
dc.identifier.thesisOJH0009_FEI_P1807_1801V001_2016
dc.rights.accessopenAccess


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