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A Two-Parameter Fractional Tsallis Decision Tree.
- Source :
-
Entropy . May2022, Vol. 24 Issue 5, pN.PAG-N.PAG. 15p. - Publication Year :
- 2022
-
Abstract
- Decision trees are decision support data mining tools that create, as the name suggests, a tree-like model. The classical C4.5 decision tree, based on the Shannon entropy, is a simple algorithm to calculate the gain ratio and then split the attributes based on this entropy measure. Tsallis and Renyi entropies (instead of Shannon) can be employed to generate a decision tree with better results. In practice, the entropic index parameter of these entropies is tuned to outperform the classical decision trees. However, this process is carried out by testing a range of values for a given database, which is time-consuming and unfeasible for massive data. This paper introduces a decision tree based on a two-parameter fractional Tsallis entropy. We propose a constructionist approach to the representation of databases as complex networks that enable us an efficient computation of the parameters of this entropy using the box-covering algorithm and renormalization of the complex network. The experimental results support the conclusion that the two-parameter fractional Tsallis entropy is a more sensitive measure than parametric Renyi, Tsallis, and Gini index precedents for a decision tree classifier. [ABSTRACT FROM AUTHOR]
- Subjects :
- *DECISION trees
*DATA mining
*ENTROPY
Subjects
Details
- Language :
- English
- ISSN :
- 10994300
- Volume :
- 24
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- Entropy
- Publication Type :
- Academic Journal
- Accession number :
- 157190535
- Full Text :
- https://doi.org/10.3390/e24050572