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Fast branch & bound algorithms for optimal feature selection.

Petr Somol1, Pavel Pudil, Josef Kittler

  • 1Department of Pattern Recognition, Institute of Information Theory and Automation of the Academy of Sciences, Czech Republic. somol@utia.cas.cz

IEEE Transactions on Pattern Analysis and Machine Intelligence
|June 27, 2008
PubMed
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A new search method for optimal feature selection using Branch & Bound significantly speeds up computations by predicting criterion values. This approach enhances algorithm efficiency and performance across various datasets.

Area of Science:

  • Computer Science
  • Machine Learning
  • Data Mining

Background:

  • Optimal feature subset selection is critical for model performance and interpretability.
  • Traditional Branch & Bound algorithms for feature selection can be computationally intensive due to exponential search spaces.

Purpose of the Study:

  • To introduce a novel search principle for optimal feature subset selection using the Branch & Bound method.
  • To improve the computational efficiency and speed of Branch & Bound algorithms for feature selection.

Main Methods:

  • Development of a simple mechanism for predicting criterion values to avoid slow evaluations.
  • Proposal of two implementations of the prediction mechanism for nonrecursive and recursive criterion forms.
  • Investigation of factors influencing Branch & Bound algorithm performance, including feature diversity, stability, and criterion function dependence.

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Main Results:

  • The proposed algorithms consistently find the optimum several times faster than existing Branch & Bound methods.
  • Algorithm speed is shown to depend significantly on feature diversity, feature stability, and criterion function dependence on subset size.
  • Identification of scenarios leading to dramatically accelerated searches, including linear time completion.

Conclusions:

  • The novel prediction mechanism substantially enhances the efficiency of Branch & Bound for optimal feature subset selection.
  • Understanding feature and criterion properties is crucial for optimizing Branch & Bound search performance.
  • The findings offer practical improvements for feature selection in machine learning and data mining applications.