Multi-Objective Genetic Programming Projection Pursuit for Exploratory Data Modeling

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Ilknur Icke and Andrew Rosenberg",
  title =        "Multi-Objective Genetic Programming Projection Pursuit
                 for Exploratory Data Modeling",
  booktitle =    "Workshop for Women in Machine Learning",
  year =         "2010",
  editor =       "Diane Oyen",
  address =      "Canada",
  month =        "6 " # dec,
  keywords =     "genetic algorithms, genetic programming, MOG3P",
  URL =          "",
  URL =          "",
  size =         "2 pages",
  abstract =     "For classification problems, feature extraction is a
                 crucial process which aims to find a suitable data
                 representation that increases the performance of the
                 machine learning algorithm. According to the curse of
                 dimensionality theorem, the number of samples needed
                 for a classification task increases exponentially as
                 the number of dimensions (variables, features)
                 increases. On the other hand, it is costly to collect,
                 store and process data. Moreover, irrelevant and
                 redundant features might hinder classifier performance.
                 In exploratory analysis settings, high dimensionality
                 prevents the users from exploring the data visually.
                 Feature extraction is a two-step process: feature
                 construction and feature selection. Feature
                 construction creates new features based on the original
                 features and feature selection is the process of
                 selecting the best features as in filter, wrapper and
                 embedded methods. In this work, we focus on feature
                 construction methods that aim to decrease data
                 dimensionality for visualisation tasks. Various linear
                 (such as principal components analysis (PCA), multiple
                 discriminants analysis (MDA), exploratory projection
                 pursuit) and non-linear (such as multidimensional
                 scaling (MDS), manifold learning, kernel PCA/LDA,
                 evolutionary constructive induction) techniques have
                 been proposed for dimensionality reduction. Our
                 algorithm is an adaptive feature extraction method
                 which consists of evolutionary constructive induction
                 for feature construction and a hybrid filter/wrapper
                 method for feature selection.",
  notes =        "WiML 2010

Genetic Programming entries for Ilknur Icke Andrew Rosenberg