A Hybrid Tsallis-Polarization Impurity Measure for Decision Trees: Theoretical Foundations and Empirical Evaluation
#decision trees #impurity measure #Tsallis entropy #polarization #classification #machine learning #empirical evaluation
📌 Key Takeaways
- A new hybrid impurity measure for decision trees combines Tsallis entropy and polarization concepts.
- The measure is designed to improve decision tree performance in classification tasks.
- Theoretical foundations of the hybrid measure are established and analyzed.
- Empirical evaluation demonstrates its effectiveness compared to traditional impurity measures.
📖 Full Retelling
arXiv:2603.13241v1 Announce Type: cross
Abstract: We introduce the Integrated Tsallis Combination (ITC), a hybrid impurity measure for decision tree learning that combines normalized Tsallis entropy with an exponential polarization component. While many existing measures sacrifice theoretical soundness for computational efficiency or vice versa, ITC provides a mathematically principled framework that balances both aspects. The core innovation lies in the complementarity between Tsallis entropy'
🏷️ Themes
Machine Learning, Data Science
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Original Source
arXiv:2603.13241v1 Announce Type: cross
Abstract: We introduce the Integrated Tsallis Combination (ITC), a hybrid impurity measure for decision tree learning that combines normalized Tsallis entropy with an exponential polarization component. While many existing measures sacrifice theoretical soundness for computational efficiency or vice versa, ITC provides a mathematically principled framework that balances both aspects. The core innovation lies in the complementarity between Tsallis entropy'
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