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A Decision Tree Classification Algorithm Based on Two-Term RS-Entropy.

Ruoyue Mao1, Xiaoyang Shi1, Zhiyan Shi1

  • 1School of Mathematical Sciences, Jiangsu University, Zhenjiang 212013, China.

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Summary
This summary is machine-generated.

This study introduces generalized entropy for decision tree algorithms, enhancing flexibility and classification accuracy. New methods, RSE and RSEIM, outperform traditional approaches by optimizing splitting criteria.

Keywords:
classificationdecision treegeneralized entropysplit criteria

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Area of Science:

  • Machine Learning
  • Information Theory

Background:

  • Decision tree algorithms are popular for classification due to accuracy and interpretability.
  • Traditional methods (ID3, C4.5, CART) use splitting criteria like Shannon entropy and Gini index, but lack flexibility.
  • Varying dataset performance makes optimal splitting criterion selection difficult.

Purpose of the Study:

  • Introduce generalized entropy as a unified splitting criterion for decision trees.
  • Propose novel decision tree algorithms: RSE (RS-Entropy) and RSEIM (RS-Entropy Information Method).
  • Enhance flexibility and classification accuracy of decision tree algorithms.

Main Methods:

  • Utilized generalized entropy from information theory as a splitting criterion.
  • Developed RSE and RSEIM algorithms with multiple free parameters for flexibility.
  • Employed genetic algorithms for parameter optimization on various datasets.

Main Results:

  • RSE and RSEIM demonstrated significantly improved classification accuracy compared to traditional methods.
  • The proposed methods did not increase the complexity of the resulting decision trees.
  • Generalized entropy offers a more flexible approach to splitting criteria.

Conclusions:

  • RSE and RSEIM algorithms represent a flexible advancement in decision tree classification.
  • The use of generalized entropy and optimized parameters leads to superior performance.
  • This work provides a more adaptable framework for decision tree construction.