A Decision Tree Based On Related Family Article Swipe
Decision trees are widely used supervised learning models known for their simplicity, interpretability, and effectiveness in classification and regression tasks. Feature selection can remove redundant and noisy features, enhancing the generalization and robustness of decision trees. However, due to the high computational cost of existing feature selection methods, it is typically applied only once before classifier training, providing the classifier with dimensionally reduced data. This limits the synergistic effect between feature selection and the construction of split nodes in decision trees. The Related Family is an efficient feature evaluation method proposed by our research team. Its efficiency allows us to use it in the construction of split nodes in decision trees, leading to better splitting criteria. Building on this method, We introduce the Dynamic Related Family Decision Tree DRFDT , which dynamically selects optimal features for each sample subgroup as the tree grows. Experiments demonstrate that DRFDT outperforms a wide range of classification algorithms across 15 UCI datasets, achieving an average accuracy of 89.30 . This represents significant improvements over classical single-feature decision tree methods CART: _3.87 , traditional classification algorithms KNN: _5.71 , SVM: _4.54 , multi-feature split decision tree algorithms CART-LC: _3.99 , O1: _4.25 , and state-of-the-art decision tree classification algorithms FGBDT: _4.88 , MPRBC: _4.77 , RSLRS: _26.84 .
Related Topics
Concepts
Decision tree
Incremental decision tree
Decision tree learning
Decision stump
Feature selection
Artificial intelligence
Machine learning
Classifier (UML)
Logistic model tree
Computer science
ID3 algorithm
Robustness (evolution)
Data mining
C4.5 algorithm
Statistical classification
Information gain ratio
Pattern recognition (psychology)
Feature (linguistics)
Mathematics
Alternating decision tree
Generalization
Tree (set theory)
Linear classifier
Decision problem
Regression
Metadata
- Type
- article
- Landing Page
- https://doi.org/10.65286/icic.v21i3.25735
- http://poster-openaccess.com/files/ICIC2025/3966.pdf
- OA Status
- gold
- OpenAlex ID
- https://openalex.org/W7104517189
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7104517189Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.65286/icic.v21i3.25735Digital Object Identifier
- Title
-
A Decision Tree Based On Related FamilyWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-01Full publication date if available
- Authors
-
Wenxing LiList of authors in order
- Landing page
-
https://doi.org/10.65286/icic.v21i3.25735Publisher landing page
- PDF URL
-
https://poster-openaccess.com/files/ICIC2025/3966.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://poster-openaccess.com/files/ICIC2025/3966.pdfDirect OA link when available
- Concepts
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Decision tree, Incremental decision tree, Decision tree learning, Decision stump, Feature selection, Artificial intelligence, Machine learning, Classifier (UML), Logistic model tree, Computer science, ID3 algorithm, Robustness (evolution), Data mining, C4.5 algorithm, Statistical classification, Information gain ratio, Pattern recognition (psychology), Feature (linguistics), Mathematics, Alternating decision tree, Generalization, Tree (set theory), Linear classifier, Decision problem, RegressionTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.interpretability, | 12 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 1 |
| citation_normalized_percentile.value | 0.90978645 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |