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Feature Selection Combining Information Theory View and Algebraic View in the Neighborhood Decision System.

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  • 1College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China.

Entropy (Basel, Switzerland)
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This study introduces a novel feature selection algorithm using neighborhood rough set theory. The method enhances classification performance by combining information and algebraic views for optimal feature subset selection.

Keywords:
algebraic viewfeature selectioninformation theory viewneighborhood rough setnon-monotonicity

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Area of Science:

  • Data Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Feature selection is crucial in rough set theory, but existing algorithms often have limitations in reduction ability and classification performance.
  • Current methods may not adequately balance information theory and algebraic perspectives for neighborhood decision systems.

Purpose of the Study:

  • To propose an improved feature selection algorithm for neighborhood decision systems.
  • To enhance classification performance by integrating information and algebraic views.

Main Methods:

  • Utilizing neighborhood relationships in the neighborhood rough set model to preserve continuous data classification information.
  • Developing neighborhood information entropy measures and defining neighborhood credibility and coverage.
  • Introducing neighborhood joint entropy by incorporating neighborhood credibility and coverage.
  • Designing a feature selection algorithm based on the proposed neighborhood joint entropy.

Main Results:

  • Experimental analysis on nine datasets demonstrated the algorithm's effectiveness.
  • The proposed algorithm successfully identified optimal feature subsets.
  • The selected feature subsets maintained or improved classification performance compared to existing methods.

Conclusions:

  • The novel feature selection algorithm effectively addresses limitations of existing methods.
  • Combining information and algebraic views within a neighborhood decision system framework yields superior results.
  • The approach offers a robust method for feature selection, enhancing data classification accuracy.