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Genetic programming for simultaneous feature selection and classifier design.

Durga Prasad Muni1, Nikhil R Pal, Jyotirmoy Das

  • 1Electronics and Communication Sciences Unit, Indian Statistical Institute, Calcutta-700108, India. muni_r@isical.ac.in

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 14, 2006
PubMed
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This study introduces an online feature selection algorithm using genetic programming (GP) that simultaneously selects features and builds classifiers. The novel GP approach yields good results and a feature ranking scheme, proving robust on diverse datasets.

Area of Science:

  • Machine Learning
  • Computational Intelligence
  • Data Mining

Background:

  • Feature selection is crucial for improving classifier performance and reducing dimensionality.
  • Existing methods may not efficiently handle high-dimensional or streaming data.
  • Genetic programming (GP) offers a flexible framework for evolutionary computation.

Purpose of the Study:

  • To develop an online feature selection algorithm using genetic programming.
  • To simultaneously select relevant features and construct a classifier.
  • To introduce novel genetic programming operators for enhanced feature selection.

Main Methods:

  • An online feature selection algorithm based on genetic programming (GP).
  • A GP methodology that constructs a c-tree classifier for c-class problems.

Related Experiment Videos

  • Introduction of two new crossover operations tailored for feature selection.
  • Evaluation on datasets with dimensions ranging from 4 to 7129.

    • Main Results:

    • The proposed GP algorithm consistently produced good results across various datasets.
    • The method demonstrated effectiveness on datasets with intentionally added redundant features.
    • A byproduct of the algorithm is a robust feature ranking scheme.
    • Performance was compared favorably against existing literature results.

    Conclusions:

    • The developed GP-based online feature selection is effective and robust.
    • The simultaneous feature selection and classifier construction approach is advantageous.
    • The novel crossover operations enhance the feature selection process.
    • The algorithm provides a valuable feature ranking mechanism for data analysis.