A Hybrid Gradient Boosting and Neural Network Model for Predicting Urban Happiness: Integrating Ensemble Learning with Deep Representation for Enhanced Accuracy
Guardat en:
| Publicat a: | Machine Learning and Knowledge Extraction vol. 7, no. 1 (2025), p. 4 |
|---|---|
| Autor principal: | |
| Altres autors: | |
| Publicat: |
MDPI AG
|
| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Etiquetes: |
Sense etiquetes, Sigues el primer a etiquetar aquest registre!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3181641154 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2504-4990 | ||
| 024 | 7 | |a 10.3390/make7010004 |2 doi | |
| 035 | |a 3181641154 | ||
| 045 | 2 | |b d20250101 |b d20250331 | |
| 100 | 1 | |a Gregorius Airlangga |u Department of Information Systems, Atma Jaya Catholic University of Indonesia, Jakarta 12930, Indonesia; <email>gregorius.airlangga@atmajaya.ac.id</email> | |
| 245 | 1 | |a A Hybrid Gradient Boosting and Neural Network Model for Predicting Urban Happiness: Integrating Ensemble Learning with Deep Representation for Enhanced Accuracy | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Urban happiness prediction presents a complex challenge, due to the nonlinear and multifaceted relationships among socio-economic, environmental, and infrastructural factors. This study introduces an advanced hybrid model combining a gradient boosting machine (GBM) and neural network (NN) to address these complexities. Unlike traditional approaches, this hybrid leverages a GBM to handle structured data features and an NN to extract deeper nonlinear relationships. The model was evaluated against various baseline machine learning and deep learning models, including a random forest, CNN, LSTM, CatBoost, and TabNet, using metrics such as RMSE, MAE, R2, and MAPE. The GBM + NN hybrid achieved superior performance, with the lowest RMSE of 0.3332, an R2 of 0.9673, and an MAPE of 7.0082%. The model also revealed significant insights into urban indicators, such as a 10% improvement in air quality correlating to a 5% increase in happiness. These findings underscore the potential of hybrid models in urban analytics, offering both predictive accuracy and actionable insights for urban planners. | |
| 653 | |a Accuracy | ||
| 653 | |a Machine learning | ||
| 653 | |a Research methodology | ||
| 653 | |a Happiness | ||
| 653 | |a Cost of living | ||
| 653 | |a Deep learning | ||
| 653 | |a Neural networks | ||
| 653 | |a Structured data | ||
| 653 | |a Well being | ||
| 653 | |a Air quality | ||
| 653 | |a Outdoor air quality | ||
| 653 | |a Econometrics | ||
| 653 | |a Decision trees | ||
| 653 | |a Ensemble learning | ||
| 700 | 1 | |a Liu, Alan |u Department of Electrical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan | |
| 773 | 0 | |t Machine Learning and Knowledge Extraction |g vol. 7, no. 1 (2025), p. 4 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3181641154/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3181641154/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3181641154/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |