Investigation of effect of reducing dataset's size on classification algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Neelam Singhal and Mohd. Ashraf",
  booktitle =    "2nd International Conference on Computing for
                 Sustainable Global Development (INDIACom)",
  title =        "Investigation of effect of reducing dataset's size on
                 classification algorithms",
  year =         "2015",
  pages =        "2036--2040",
  month =        "11-13 " # mar,
  isbn13 =       "978-9-3805-4415-1",
  keywords =     "genetic algorithms, genetic programming, Decision
                 Tree, Naive Bayes, K-Nearest Neighbour, Genetic
                 Programming, Accuracy",
  URL =          "",
  size =         "5 pages",
  abstract =     "Data mining is now one of the most active field of
                 research. Extracting those nuggets of information is
                 becoming crucial and one of its important technique is
                 classification. It helps to group the data in some
                 predefined classes. Various techniques for
                 classification exists which classifies the data using
                 different algorithms. Each algorithm has its own area
                 of best and worst performance. This paper concentrates
                 on the four most famous algorithms, i.e., Decision
                 Tree, Naive Bayes, K Nearest Neighbour and Genetic
                 Programming and the effect on their performance of time
                 and accuracy when the number of instances are
                 incrementally decreased. This paper will also
                 investigate the difference in result when working with
                 binary class or multiclass datasets and suggest the
                 algorithms to follow when using certain kind of
  notes =        "Soybean, Ecoli, Wisconsin-Breast-cancer

                 School of ICT, Gautam Buddha University, Greater Noida,

                 Also known as \cite{7100598}",

Genetic Programming entries for Neelam Singhal Mohd Ashraf