Unbiased Isotonic Regression Tree for Discovering Hidden Heterogeneity in Monotonicity Constraints
Integrating domain knowledge is increasingly recognized as vital for improving the relevance and reliability of machine learning models. This integration is often implemented through specific types of constraints that reflect real-world conditions or theoretical insights. Within the family of regres...
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MDPI AG
2025-01-01
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| author | Doowon Choi |
| author_facet | Doowon Choi |
| author_sort | Doowon Choi |
| collection | DOAJ |
| description | Integrating domain knowledge is increasingly recognized as vital for improving the relevance and reliability of machine learning models. This integration is often implemented through specific types of constraints that reflect real-world conditions or theoretical insights. Within the family of regression trees, the isotonic regression tree is used to incorporate a monotonicity constraint between a predictor and a response variable. However, the isotonic regression tree could be susceptible to split selection bias, as it selects both its split variable and cutpoint simultaneously. This study first explores the possibility of selection bias on split variables in the isotonic regression tree and proposes an unbiased isotonic regression tree that mitigates the issue of the selection bias problem. The results of the simulation and case study demonstrate the effectiveness of the proposed approach and the ability to discover hidden heterogeneous monotonic constraints. |
| format | Article |
| id | doaj-art-6c32e42ffb1c4b6a861709b28efa79cf |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-6c32e42ffb1c4b6a861709b28efa79cf2025-01-24T13:20:56ZengMDPI AGApplied Sciences2076-34172025-01-0115281810.3390/app15020818Unbiased Isotonic Regression Tree for Discovering Hidden Heterogeneity in Monotonicity ConstraintsDoowon Choi0Department of Data Science, Inha University, Incheon 22212, Republic of KoreaIntegrating domain knowledge is increasingly recognized as vital for improving the relevance and reliability of machine learning models. This integration is often implemented through specific types of constraints that reflect real-world conditions or theoretical insights. Within the family of regression trees, the isotonic regression tree is used to incorporate a monotonicity constraint between a predictor and a response variable. However, the isotonic regression tree could be susceptible to split selection bias, as it selects both its split variable and cutpoint simultaneously. This study first explores the possibility of selection bias on split variables in the isotonic regression tree and proposes an unbiased isotonic regression tree that mitigates the issue of the selection bias problem. The results of the simulation and case study demonstrate the effectiveness of the proposed approach and the ability to discover hidden heterogeneous monotonic constraints.https://www.mdpi.com/2076-3417/15/2/818isotonic regression treemonotonically increasing constraintssubgroup identificationunbiased split variable selection |
| spellingShingle | Doowon Choi Unbiased Isotonic Regression Tree for Discovering Hidden Heterogeneity in Monotonicity Constraints Applied Sciences isotonic regression tree monotonically increasing constraints subgroup identification unbiased split variable selection |
| title | Unbiased Isotonic Regression Tree for Discovering Hidden Heterogeneity in Monotonicity Constraints |
| title_full | Unbiased Isotonic Regression Tree for Discovering Hidden Heterogeneity in Monotonicity Constraints |
| title_fullStr | Unbiased Isotonic Regression Tree for Discovering Hidden Heterogeneity in Monotonicity Constraints |
| title_full_unstemmed | Unbiased Isotonic Regression Tree for Discovering Hidden Heterogeneity in Monotonicity Constraints |
| title_short | Unbiased Isotonic Regression Tree for Discovering Hidden Heterogeneity in Monotonicity Constraints |
| title_sort | unbiased isotonic regression tree for discovering hidden heterogeneity in monotonicity constraints |
| topic | isotonic regression tree monotonically increasing constraints subgroup identification unbiased split variable selection |
| url | https://www.mdpi.com/2076-3417/15/2/818 |
| work_keys_str_mv | AT doowonchoi unbiasedisotonicregressiontreefordiscoveringhiddenheterogeneityinmonotonicityconstraints |