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Application of Improved DEA Algorithm in Public Management Problem Classification.

Jialin Li1

  • 1Vanderbilt University, Nashville, TN 37240, USA.

Computational Intelligence and Neuroscience
|September 29, 2022
PubMed
Summary
This summary is machine-generated.

This study improves public management efficiency using Data Envelopment Analysis (DEA) and classification algorithms. The enhanced DEA model provides more reasonable performance evaluations for public management departments.

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Area of Science:

  • Public Administration
  • Operations Research
  • Data Science

Background:

  • Public management faces challenges in performance evaluation.
  • Existing Data Envelopment Analysis (DEA) methods have limitations, including the "relative effectiveness" defect.

Purpose of the Study:

  • To propose an improved algorithm for public management performance evaluation.
  • To overcome the limitations of traditional DEA methods.
  • To enhance the accuracy and reasonableness of performance evaluations.

Main Methods:

  • Developed an improved algorithm combining Data Envelopment Analysis (DEA) and classification algorithms.
  • Utilized lever management to address DEA's "relative effectiveness" defect.
  • Employed principal component analysis to mitigate the impact of input and output indicators.
  • Conducted empirical analysis on public management department performance.

Main Results:

  • The improved DEA model demonstrated enhanced performance evaluation capabilities.
  • A high correlation coefficient (0.977759) was observed between initial and optimized efficiency values.
  • The optimized evaluation results were found to be more reasonable compared to the initial system.

Conclusions:

  • The proposed algorithm effectively improves public management performance evaluation.
  • The integration of DEA, classification algorithms, and principal component analysis offers a robust solution.
  • The enhanced method provides more reliable and reasonable assessments for public management departments.