A Multiobjective Genetic Programming-Based Ensemble for Simultaneous Feature Selection and Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Nag:2015:Cybernetics,
  author =       "Kaustuv Nag and Nikhil R. Pal",
  journal =      "IEEE Transactions on Cybernetics",
  title =        "A Multiobjective Genetic Programming-Based Ensemble
                 for Simultaneous Feature Selection and Classification",
  year =         "2015",
  keywords =     "genetic algorithms, genetic programming,
                 Classification, ensemble, feature selection (FS),
                 genetic programming (GP)",
  DOI =          "doi:10.1109/TCYB.2015.2404806",
  ISSN =         "2168-2267",
  size =         "12 pages",
  abstract =     "We present an integrated algorithm for simultaneous
                 feature selection (FS) and designing of diverse
                 classifiers using a steady state multiobjective genetic
                 programming (GP), which minimises three objectives: 1)
                 false positives (FPs); 2) false negatives (FNs); and 3)
                 the number of leaf nodes in the tree. Our method
                 divides a c-class problem into c binary classification
                 problems. It evolves c sets of genetic programs to
                 create c ensembles. During mutation operation, our
                 method exploits the fitness as well as unfitness of
                 features, which dynamically change with generations
                 with a view to using a set of highly relevant features
                 with low redundancy. The classifiers of i-th class
                 determine the {net belongingness} of an unknown data
                 point to the i'th class using a weighted voting scheme,
                 which makes use of the FP and FN mistakes made on the
                 training data. We test our method on eight microarray
                 and 11 text data sets with diverse number of classes
                 (from 2 to 44), large number of features (from 2000 to
                 49,151), and high feature-to-sample ratio (from 1.03 to
                 273.1). We compare our method with a bi-objective GP
                 scheme that does not use any FS and rule size reduction
                 strategy. It depicts the effectiveness of the proposed
                 FS and rule size reduction schemes. Furthermore, we
                 compare our method with four classification methods in
                 conjunction with six features selection algorithms and
                 full feature set. Our scheme performs the best for 380
                 out of 474 combinations of data sets, algorithm and FS
                 method.",
  notes =        "Entered for 2016 HUMIES Also known as \cite{7055929}",
}

Genetic Programming entries for Kaustuv Nag Nikhil Ranjan Pal

Citations