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Feature Selection based on the Local Lift Dependence Scale.

Diego Marcondes1, Adilson Simonis1, Junior Barrera1

  • 1Institute of Mathematics and Statistics, University of São Paulo, São Paulo 05508-090, Brazil.

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Summary
This summary is machine-generated.

This study introduces a new feature selection method by extending the search space to include feature values. This approach, using the Local Lift Dependence Scale (LLDS), better analyzes joint distributions for improved feature selection.

Keywords:
feature selectionlocal lift dependence scalemutual informationvariable dependencevariable selection

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Area of Science:

  • Machine Learning
  • Data Mining
  • Statistical Modeling

Background:

  • Classical feature selection relies on minimizing cost functions on joint distributions within the Boolean lattice of features sets (BLFS).
  • Existing methods may overlook the influence of specific feature values on target variables.

Purpose of the Study:

  • To extend the feature selection search space beyond feature sets to include feature values.
  • To introduce and apply the Local Lift Dependence Scale (LLDS) for analyzing local properties of joint distributions.
  • To improve feature selection by considering feature value dependencies.

Main Methods:

  • Extended the optimization search space from the Boolean lattice of features sets (BLFS) to a collection of Boolean lattices of ordered pairs (CBLOP).
  • Developed and applied a local formulation of Shannon's mutual information to generate the Local Lift Dependence Scale (LLDS).
  • Utilized LLDS to analyze local properties of joint distributions, capturing dependencies neglected by global measures.

Main Results:

  • The LLDS effectively characterizes variable dependence at multiple resolutions, offering a more granular analysis than traditional methods.
  • The extended search space allows for the selection of not only relevant features but also their influential values.
  • Demonstrated successful application in diverse domains: student performance prediction, political voting analysis, and terrain property analysis.

Conclusions:

  • The proposed method, leveraging LLDS and an expanded search space, provides a more comprehensive approach to feature selection.
  • This technique enhances the ability to identify key features and their specific values that influence target variables.
  • The findings suggest significant potential for improved predictive modeling and data analysis across various scientific fields.