Tree-Structured Model with Unbiased Variable Selection and Interaction Detection for Ranking Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset
2.2. Variable Selection
Algorithm 1 Main effect detection. |
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Algorithm 2 Interaction effect detection. |
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2.3. Point Selection and Stopping Rule
3. Simulation Studies on Variable Selection
3.1. Independent Case
3.2. Dependent Models
4. Data Analysis
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Group | Country |
---|---|
1 | Italy (113), the Netherlands (48), Denmark (95) |
2 | France (106), Spain (35), Belgium (115), Croatia (24), Greece (53) |
3 | West Germany (52), East Germany (48), Iceland (62), Czechia (144), |
Romania (60), Bulgaria (64), Malta (29), Luxembourg (32), Slovenia (49) | |
4 | Estonia (70), Latvia (66), Lithuania (50), Poland (109), Slovakia (103), |
Hungary (77), Russia (170), Ukraine (66), Belarus (71) |
Model | Condition |
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I | |
II | |
II | |
IV | |
V |
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Shih, Y.-S.; Kung, Y.-H. Tree-Structured Model with Unbiased Variable Selection and Interaction Detection for Ranking Data. Mach. Learn. Knowl. Extr. 2023, 5, 448-459. https://doi.org/10.3390/make5020027
Shih Y-S, Kung Y-H. Tree-Structured Model with Unbiased Variable Selection and Interaction Detection for Ranking Data. Machine Learning and Knowledge Extraction. 2023; 5(2):448-459. https://doi.org/10.3390/make5020027
Chicago/Turabian StyleShih, Yu-Shan, and Yi-Hung Kung. 2023. "Tree-Structured Model with Unbiased Variable Selection and Interaction Detection for Ranking Data" Machine Learning and Knowledge Extraction 5, no. 2: 448-459. https://doi.org/10.3390/make5020027
APA StyleShih, Y. -S., & Kung, Y. -H. (2023). Tree-Structured Model with Unbiased Variable Selection and Interaction Detection for Ranking Data. Machine Learning and Knowledge Extraction, 5(2), 448-459. https://doi.org/10.3390/make5020027