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Related Experiment Video

Updated: Sep 21, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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A Two-Parameter Fractional Tsallis Decision Tree.

Jazmín S De la Cruz-García1, Juan Bory-Reyes2, Aldo Ramirez-Arellano1

  • 1SEPI-UPIICSA, Instituto Politécnico Nacional, Mexico City C.P. 08400, Mexico.

Entropy (Basel, Switzerland)
|May 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel decision tree classifier using a two-parameter fractional Tsallis entropy. This method efficiently calculates parameters for complex networks, outperforming classical decision trees.

Keywords:
Gini indexcomplex networksdecision treestwo-parameter Tsallis entropy

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Area of Science:

  • Data Mining
  • Machine Learning
  • Information Theory

Background:

  • Decision trees are vital data mining tools, with C4.5 using Shannon entropy for attribute splitting.
  • Classical methods for optimizing entropy parameters (Tsallis, Renyi) are computationally intensive for large datasets.

Purpose of the Study:

  • To introduce a more efficient decision tree classifier using a two-parameter fractional Tsallis entropy.
  • To develop a method for efficient parameter computation in large-scale databases.

Main Methods:

  • Representing databases as complex networks.
  • Utilizing the box-covering algorithm and network renormalization for parameter calculation.
  • Implementing a decision tree based on two-parameter fractional Tsallis entropy.

Main Results:

  • The proposed two-parameter fractional Tsallis entropy offers a more sensitive measure for decision tree classification.
  • The complex network approach enables efficient parameter computation, overcoming limitations of traditional methods.
  • Experimental results demonstrate superior performance compared to Renyi, Tsallis, and Gini index-based classifiers.

Conclusions:

  • The two-parameter fractional Tsallis entropy provides an effective and efficient alternative for decision tree classification.
  • The constructionist approach using complex networks significantly improves computational feasibility for large datasets.
  • This novel method enhances the sensitivity and performance of decision tree classifiers.