Optimal column subset selection for image classification by genetic algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Pavel Kromer and Jan Platos and Jana Nowakova and 
                 Vaclav Snasel",
  title =        "Optimal column subset selection for image
                 classification by genetic algorithms",
  journal =      "Annals of Operations Research",
  year =         "2018",
  volume =       "265",
  number =       "2",
  pages =        "205--222",
  keywords =     "genetic algorithms, genetic programming,
                 Classification, Column subset selection, Dimensionality
                 reduction, Feature selection, Galgorithms",
  URL =          "https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991018113&doi=10.1007%2fs10479-016-2331-0&partnerID=40&md5=0d6022f913b66428010164c45edd4bea",
  DOI =          "doi:10.1007/s10479-016-2331-0",
  source =       "Scopus",
  affiliation =  "Department of Computer Science, Faculty of Electrical
                 Engineering and Computer Science, VSB-Technical
                 University of Ostrava, 17. Listopadu 15/2172,
                 Ostrava-Poruba, Czech Republic",
  abstract =     "Many problems in operations research can be solved by
                 combinatorial optimization. Fixed-length subset
                 selection is a family of combinatorial optimization
                 problems that involve selection of a set of unique
                 objects from a larger superset. Feature selection,
                 p-median problem, and column subset selection problem
                 are three examples of hard problems that involve search
                 for fixed-length subsets. Due to their high complexity,
                 exact algorithms are often infeasible to solve
                 real-world instances of these problems and approximate
                 methods based on various heuristic and metaheuristic
                 (e.g. nature-inspired) approaches are often employed.
                 Selecting column subsets from massive data matrices is
                 an important technique useful for construction of
                 compressed representations and low rank approximations
                 of high-dimensional data. Search for an optimal subset
                 of exactly k columns of a matrix, Am by n, k less than
                 n, is a well-known hard optimization problem with
                 practical implications for data processing and mining.
                 It can be used for unsupervised feature selection,
                 dimensionality reduction, data visualization, and so
                 on. A compressed representation of raw real-world data
                 can contribute, for example, to reduction of algorithm
                 training times in supervised learning, to elimination
                 of overfitting in classification and regression, to
                 facilitation of better data understanding, and to many
                 other benefits. This paper proposes a novel genetic
                 algorithm for the column subset selection problem and
                 evaluates it in a series of computational experiments
                 with image classification. The evaluation shows that
                 the proposed modifications improve the results obtained
                 by artificial evolution.",

Genetic Programming entries for Pavel Kromer Jan Platos Jana Nowakova Vaclav Snasel