Feature Selection and Function Approximation Using Adaptive Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

@PhdThesis{Ojha:thesis,
  author =       "Varun Kumar Ojha",
  title =        "Feature Selection and Function Approximation Using
                 Adaptive Algorithms",
  school =       "Faculty of Electrical Engineering and Computer Science
                 (FEI), Technical University of Ostrava",
  year =         "2016",
  month =        "26 " # sep,
  keywords =     "genetic algorithms, genetic programming, feedforward
                 neural network, fuzzy inference system, multiobjective,
                 metaheuristics, ensemble learning, feature selection",
  language =     "en",
  URL =          "http://hdl.handle.net/10084/112274",
  URL =          "http://dspace.vsb.cz/bitstream/handle/10084/112274/OJH0009_FEI_P1807_1801V001_2016.pdf",
  abstract =     "Multiobjective heterogeneous flexible neural tree
                 (HFNT) and multi-objective 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 multi objective 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 modelling 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.",
  notes =        "restricted access. Is this GP?

                 Supervisor Vaclav Snasel",
}

Genetic Programming entries for Varun Kumar Ojha

Citations