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---
pretty_name: Tabular Imbalanced Regression Datasets
tags:
- tabular
- imbalanced-regression
- regression
- benchmark
- machine-learning
viewer: false
license: cc-by-4.0
datasets:
- UCI
- Kaggle
- Github
language:
- en
---
# Tabular Imbalanced Regression Datasets
## Repository Summary
This repository provides a collection of **81 tabular datasets** curated for research on **tabular imbalanced regression** problems.
They were obtained from the various studies carried out on the subject (source and papers listed below).
Its objective is to centralize datasets commonly used in the literature, serving as a solid reference point for future work.
Additional datasets can be contributed or requested — feel free to open an issue or pull request.
## Citation
If you use dataset from this repository in your research, please cite our paper:
Samuel Stocksieker, Denys Pommeret. A Comprehensive Survey on Imbalanced Regression: Definitions, Solutions, and Future Directions. 2025. ⟨hal-05213741⟩
https://hal.science/hal-05213741
## Dataset Structure
This collection is intended for training, evaluating, and benchmarking models in **imbalanced regression** tasks.
All datasets have been preprocessed to consistently place the **target variable as the first column**.
A detailed summary of the 81 datasets is available in the table below. A second table lists the datasets used by each referenced paper in the survey.
This metadata enables standardized comparisons and better understanding of dataset difficulty and imbalance characteristics.
## Python Utilities
This repository includes Python code to compute imbalance coefficient based on the target distribution.
The script `imbalance_coefficient.py` provides the function `imb_coef()` for both continuous and discrete targets.
You can explore our Python implementation and a usage notebook in the `imbalance_coefficient/` subfolder:
- [imbalance_metric.py](./Imbalance_Coefficient/imbalance_coefficient.py): Python function to compute the imbalance ratio.
- [Demo Notebook](./Imbalance_Coefficient/imb_coef_demo.ipynb): notebook demonstration.
## Dataset Overview
For each dataset, the following metadata is provided:
- **n_obs**: Number of observations
- **p_var**: Total number of features
- **p_num**: Number of numerical features
- **p_cat**: Number of categorical features
- **Type**: Type of target variable (continuous, integer, etc.)
- **Skew**: Skewness of the target distribution
- **Imb. Coef.**: Imbalance coefficient as defined in Section \ref{imbCoef}
- **mIR**: Mean Imbalanced Ratio, as introduced by [Wibbeke et al., 2025](https://arxiv.org/abs/2401.12345)
- **Miss.**: Proportion of missing values
- **Used**: Number of times the dataset has been used in published papers
|dataset |n_obs |p_variables|p_numeric|p_categorical|target_type|target_skewness|Imb_coef|mIR |missing_rate|Used|
|----------------------|------|-----------|---------|-------------|-----------|---------------|---------|--------|------------|----|
|abalone |4177 |11 |11 |0 |int64 |1.11 |54.23 |345.73 |0.0 |32 |
|boston |506 |14 |14 |0 |float64 |1.1 |36.0 |218.31 |0.0 |30 |
|accel |1732 |23 |23 |0 |float64 |0.77 |51.58 |292.14 |0.0 |23 |
|availPwr |1802 |16 |9 |7 |int64 |1.9 |55.97 |348.26 |0.0 |23 |
|cpusm |8192 |13 |13 |0 |int64 |-3.42 |63.69 |358.7 |0.0 |23 |
|a7 |198 |12 |9 |3 |float64 |3.72 |70.01 |456.38 |0.0 |22 |
|bank8fm |4499 |9 |9 |0 |float64 |1.08 |44.18 |252.74 |0.0 |22 |
|a1 |198 |12 |9 |3 |float64 |1.46 |46.56 |242.44 |0.0 |20 |
|airfoild |1503 |6 |6 |0 |float64 |-0.42 |31.86 |189.28 |0.0 |18 |
|fuelCons |1764 |38 |26 |12 |float64 |1.14 |52.08 |303.6 |0.0 |18 |
|maxTorque |1802 |33 |20 |13 |int64 |1.63 |55.67 |331.58 |0.0 |18 |
|a2 |198 |12 |9 |3 |float64 |2.41 |61.29 |365.26 |0.0 |16 |
|a3 |198 |12 |9 |3 |float64 |2.47 |62.7 |391.33 |0.0 |16 |
|a4 |198 |12 |9 |3 |float64 |5.96 |77.89 |624.82 |0.0 |15 |
|a6 |198 |12 |9 |3 |float64 |3.14 |67.32 |453.93 |0.0 |15 |
|heat |7400 |12 |12 |0 |float64 |1.63 |56.2 |365.29 |0.0 |14 |
|a5 |198 |12 |9 |3 |float64 |2.33 |59.89 |335.47 |0.0 |13 |
|machineCpu |209 |7 |7 |0 |int64 |3.86 |68.46 |487.1 |0.0 |13 |
|mortgage |1049 |16 |16 |0 |float64 |1.03 |33.32 |198.8 |0.0 |13 |
|servo |167 |5 |3 |2 |float64 |1.77 |54.11 |305.16 |0.0 |12 |
|treasury |1049 |16 |16 |0 |float64 |1.33 |43.97 |242.9 |0.0 |11 |
|deltaAilerons |7129 |6 |6 |0 |float64 |-0.15 |43.91 |258.89 |0.0 |10 |
|forestFires |517 |13 |13 |0 |float64 |12.81 |90.15 |1824.05 |0.0 |10 |
|fremotor1prem0304a_sev|51949 |31 |17 |14 |int64 |170.33 |98.64 |57674.8 |4.34 |10 |
|dataset_Facebook |500 |19 |18 |1 |int64 |9.68 |84.84 |1234.7 |0.06 |9 |
|debutanizer |2394 |8 |8 |0 |float64 |1.71 |48.21 |291.81 |0.0 |9 |
|strikes |625 |7 |7 |0 |int64 |6.4 |78.86 |837.8 |0.0 |9 |
|student-mat |395 |33 |16 |17 |int64 |0.24 |24.1 |157.34 |0.0 |9 |
|ailerons |13750 |41 |41 |0 |float64 |-1.35 |57.42 |363.56 |0.0 |8 |
|wine_quality |6497 |12 |12 |0 |float64 |0.19 |60.26 |515.63 |0.0 |8 |
|autoPrice |205 |26 |16 |10 |int64 |1.79 |48.82 |266.97 |0.0 |7 |
|elevators |16599 |19 |19 |0 |int64 |0.15 |44.14 |248.2 |0.0 |7 |
|baseball |337 |17 |17 |0 |int64 |1.16 |42.7 |244.45 |0.0 |6 |
|californiaHousing |20640 |9 |9 |0 |int64 |0.98 |31.16 | |0.0 |6 |
|triazines |186 |61 |61 |0 |float64 |-1.34 |37.29 |217.24 |0.0 |6 |
|analcatdata_apnea3 |450 |12 |12 |0 |float64 |5.0 |82.68 |859.26 |0.0 |5 |
|cpuAct |8192 |22 |22 |0 |int64 |-3.42 |63.69 |358.7 |0.0 |5 |
|diabetes |43 |3 |3 |0 |float64 |-0.23 |29.83 |196.25 |0.0 |5 |
|house8H |22784 |9 |8 |1 |int64 |3.75 |69.98 | |0.0 |5 |
|kinematics8fh |8192 |9 |9 |0 |float64 |-0.4 |45.58 |256.79 |0.0 |5 |
|laser |993 |5 |5 |0 |int64 |1.19 |42.06 |245.05 |0.0 |5 |
|musicorigin |1059 |118 |118 |0 |float64 |3.21 |66.67 |486.74 |0.0 |5 |
|pollen |3848 |5 |5 |0 |float64 |-0.13 |42.93 |245.29 |0.0 |5 |
|space_ga |3107 |7 |7 |0 |float64 |-1.02 |71.07 |496.47 |0.0 |5 |
|wages |534 |11 |4 |7 |float64 |1.69 |56.78 |329.52 |0.0 |5 |
|ele-1 |495 |3 |3 |0 |float64 |1.51 |49.24 |275.72 |0.0 |4 |
|ele-2 |1056 |5 |5 |0 |float64 |1.44 |44.31 |261.91 |0.0 |4 |
|quake |2178 |4 |4 |0 |float64 |1.3 |50.91 |286.97 |0.0 |4 |
|sulfur |10081 |6 |6 |0 |float64 |2.5 |73.58 |738.32 |0.0 |4 |
|NO2Emissions |500 |8 |8 |0 |float64 |-0.55 |44.96 |266.89 |0.0 |3 |
|wankara |1609 |10 |10 |0 |float64 |0.02 |17.64 |135.47 |0.0 |3 |
|energy |19735 |28 |28 |0 |int64 |3.39 |74.6 |990.58 |0.0 |2 |
|superconductivity |21263 |82 |82 |0 |float64 |0.86 |46.97 |301.53 |0.0 |2 |
|AmesHousing |2930 |82 |39 |43 |int64 |1.74 |58.78 | |6.55 |1 |
|AutoBi |1340 |8 |8 |0 |float64 |25.69 |94.24 |4290.81 |2.85 |1 |
|avocado |18249 |13 |10 |3 |float64 |0.58 |45.34 |250.35 |0.0 |1 |
|cocomo_numeric |60 |57 |57 |0 |float64 |2.61 |60.26 |321.34 |0.0 |1 |
|College |777 |19 |17 |2 |int64 |-0.11 |37.58 |213.18 |0.0 |1 |
|communitiesCrime |1994 |127 |127 |0 |float64 |1.52 |42.98 |246.4 |0.0 |1 |
|concreteStrength |1030 |9 |9 |0 |float64 |0.42 |25.74 |166.68 |0.0 |1 |
|delta_ailerons |7129 |6 |6 |0 |float64 |0.29 |68.04 |580.68 |0.0 |1 |
|delta_elv |9517 |7 |7 |0 |float64 |0.16 |38.24 |215.0 |0.0 |1 |
|electrical |10000 |14 |13 |1 |float64 |-0.0 |3.66 |105.7 |0.0 |1 |
|hour |17379 |17 |16 |1 |int64 |1.28 |47.0 |273.61 |0.0 |1 |
|house |22784 |17 |17 |0 |float64 |3.75 |69.98 | |0.0 |1 |
|housing |1460 |81 |38 |43 |int64 |1.88 |58.35 | |6.62 |1 |
|Housing_2 |545 |13 |6 |7 |int64 |1.21 |43.52 | |0.0 |1 |
|insurance |1338 |7 |4 |3 |float64 |1.51 |49.56 |276.45 |0.0 |1 |
|kdd_coil_1 |316 |19 |19 |0 |float64 |1.45 |45.75 |243.23 |0.0 |1 |
|lungcancer_shedden |442 |25 |25 |0 |float64 |0.94 |40.87 |216.06 |0.0 |1 |
|meta |528 |66 |66 |0 |float64 |14.65 |92.53 |1880.28 |0.0 |1 |
|pdgfr |79 |321 |321 |0 |float64 |-0.64 |25.99 |171.82 |0.0 |1 |
|pendigits |10992 |17 |17 |0 |int64 |0.03 |10.25 |110.63 |0.0 |1 |
|PricingGame |100021|20 |14 |6 |float64 |7.4 |89.52 |8627.91 |0.0 |1 |
|puma32h |8192 |33 |33 |0 |float64 |-0.02 |4.6 |107.38 |0.0 |1 |
|qsar_aquatic_toxicity |546 |9 |9 |0 |float64 |0.32 |38.27 |223.42 |0.0 |1 |
|red_wine |1599 |12 |12 |0 |int64 |0.22 |52.19 |392.51 |0.0 |1 |
|sensory |576 |12 |12 |0 |float64 |-0.04 |37.04 |219.66 |0.0 |1 |
|SynchronousMachine |557 |5 |5 |0 |float64 |0.0 |8.22 |113.58 |0.0 |1 |
|telematics_syn-032021 |100000|51 |43 |8 |float64 |22.7 |96.34 |28783.35|0.0 |1 |
|yacht_hydrodynamics |308 |7 |7 |0 |float64 |1.75 |52.68 |289.73 |0.0 |1 |
The table below summarizes the papers analyzed in the survey. For each work, it includes the name of the proposed algorithm (if any), the programming language used (R or Python), the repository link (when available), and the list of datasets used.
|Paper |Algorithm |Repo |R vs python |Repo link |Dataset6 |Dataset7 |Dataset8 |Dataset9 |Dataset10 |Dataset11 |Dataset12 |Dataset13 |Dataset14 |Dataset15 |Dataset16 |Dataset17 |Dataset18 |Dataset19 |Dataset20 |Dataset21 |Dataset22 |Dataset23 |Dataset24 |Dataset25 |Dataset26 |Dataset27 |Dataset28 |Dataset29 |Dataset30 |Dataset31 |Dataset32 |Dataset33 |Dataset34 |Dataset35 |Dataset36 |Dataset37 |Dataset38 |Dataset39 |Dataset40 |Dataset41 |Dataset42 |Dataset43 |Dataset44 |Dataset45 |Dataset46|Dataset47 |
|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------|------------------|------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------|---------------------|-------------------------------|--------------|-------------|--------------------------|-----------------|-----------------|----------------------|--------------------|--------------|-------------------|-----------------|-------------------|--------------|--------------------|----------------|---------------------|-----------------|-------------------|-----------------|-------------|-----------------|----------------|-----------------|-------------|------------------|-------------|--------------|--------------|----------|----------|-------------|--------|--------|-----------------|--------|---------------|--------|--------|-------|--------|
|Predicting Outliers | |❌ | |❌ |servo |triazines |a1 |a2 |a3 |a4 |a5 |a6 |a7 |machinecpu |china |Boston |onekm |cw.drag |co2.emission |accel |availpwr |bank8FM |deltaailerons |ibm |cpuSm |deltaelv |calhousing |add |fried | | | | | | | | | | | | | | | | | |
|Rule-Based Prediction of Rare Extreme Values | |❌ | |❌ |servo |triazines |a1 |a2 |a3 |a4 |a5 |a6 |a7 |machinecpu |china |sard0 |sard2 |sard3 |sard4 |sard5 |sard0.new |sard1.new |Boston |onekm |cw.drag |co2.emission |accel |availpwr | | | | | | | | | | | | | | | | | | |
|Predicting Rare Extreme Values | |❌ | |❌ |a1 |a2 |a3 |a4 |a5 |a6 |a7 |Boston |machinecpu |bank8FM |deltaailerons |ibm |Abalone |cpuSm |servo |cw.drag |co2.emission |availpwr |china |add | | | | | | | | | | | | | | | | | | | | | | |
|Utility-Based Regression | |❌ | |❌ |International_Business_Machines| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Utility-based Performance Measures for Regression | |❌ | |❌ | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Precision and Recall for Regression | |❌ | |❌ |International_Business_Machines|Coca.Cola |Boeing |General_Motors| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Utility-based Regression | |❌ | |✅ |NO2 |miscellaneous_domains|Harmful_a_Blooms | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|An extended tuning method for cost-sensitive regression and forecasting | |❌ | |NA | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|SMOTE for Regression |SmoteR |✅|R |https://www.dcc.fc.up.pt/~ltorgo/EPIA2013/ |a1 |a2 |a3 |a4 |a5 |a6 |a7 |Abalone |Accel |dAiler |availPwr |bank8FM |cpuSm |deltaelv |fuelCons |boston |maxtorqueq | | | | | | | | | | | | | | | | | | | | | | | | | |
|Imbalanced learning: foundations, algorithms, and applications | | | |NA | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Resampling strategies for regression |over- ; under- ; SmoteR |✅|Resampling strategies for regression|http://www.dcc.fc.up.pt/~ltorgo/ExpertSystems + https://www.dcc.fc.up.pt/~ltorgo/Regression/DataSets.html + http://www.erudit.de/erudit/ |a1 |a2 |a3 |a4 |a5 |a6 |a7 |Abalone |Accel |dAiler |availPwr |bank8FM |cpuSm |deltaelv |fuelCons |boston |maxtorqueq |Heat | | | | | | | | | | | | | | | | | | | | | | | | |
|Learning from imbalanced data: open challenges and future directions | |❌ | |NA | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|UBL: an R Package for Utility-Based Learning |BaggingRegress ; EvalRegressMetrics ; GaussNoiseRegress ; RandOverRegress ; RandUnderRegress ; SMOGNRegress ; SmoteRegress ; WERCSRegress|✅|R |❌ | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|A Survey of Predictive Modeling on Imbalanced Domains | |❌ | |https://archive.ics.uci.edu/ml/datasets/Hepatitis |Hepatitis | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Learning from imbalanced data for predicting the number of software defects | |❌ | |❌ |Ant |Camel |Jedit |Synapse |Xalan |Log4j | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Learning Through Utility Optimization in Regression Tasks |MU ; NMU |✅|R |https://lib.stat.cmu.edu/datasets/ + https://github.com/paobranco/UtilityOptimizationRegression |servo |a6 |Abalone |machineCpu |a3 |a4 |a1 |a7 |boston |a2 |a5 |fuelCons |availPwr |bank8FM |Accel |airfoild |LNO2Emissions | | | | | | | | | | | | | | | | | | | | | | | | | |
|Evaluation of Ensemble Methods in Imbalanced Regression Tasks |NA |✅|R |https://github.com/nunompmoniz/Ensembles_LIDTA2017 |a3 |a6 |a4 |a7 |Abalone |a1 |boston |a5 |availPwr |a2 |cpuSm |heat |fuelCons |maxtorqueq |deltaelv |bank8FM |dAiler |Accel |ConcrStr |airfoild | | | | | | | | | | | | | | | | | | | | | | |
|SMOGN: a Pre-processing Approach for Imbalanced Regression |SMOGN |✅|R |https://github.com/paobranco/SMOGN-LIDTA17 |servo |a6 |Abalone |machineCpu |a3 |a4 |a1 |a7 |boston |a2 |a5 |fuelCons |availPwr |cpuSm |maxtorqueq |bank8FM |dAiler |ConcrStr |Accel |airfoild | | | | | | | | | | | | | | | | | | | | | | |
|Exploring Resampling with Neighborhood Bias on Imbalanced Regression Problems |Under-sampling with neighborhood bias ; Over-sampling with neighborhood bias |✅|R |https://github.com/paobranco/NeighborhoodBiasResamplingRegression |servo |a6 |Abalone |machinecpu |a3 |a4 |a1 |a7 |boston |a2 |fuelCons |availPwr |cpuSm |maxtorqueq |bank8FM |ConcrStr |Accel |airfoild | | | | | | | | | | | | | | | | | | | | | | | | |
|SMOTEBoost for Regression: Improving the Prediction of Extreme Values |SMOTEBoost |✅|R |https://github.com/nunompmoniz/DSAA2018 |airport |diabetes |a1 |a7 |autoPrice |baseball |elecLen1 |boston |forestFires |wages |strikes |laser |concrstr |mortgage |treasury |elecLen2 |musicorigin |availpwr |maxtorqueq |communitiesCrime |debutenizer |space |pollen |abalone |wine |deltaailerons|heat |bank32 |cpuAct |kinematics32fh|pumaRobot | | | | | | | | | | | |
|Pre-processing approaches for imbalanced distributions in regression |WERCS |✅|R |https://github.com/paobranco/Pre-processingApproachesImbalanceRegression + https://paobranco.github.io/DataSets-IR/ |a6 |Abalone |a3 |a4 |a1 |a7 |boston |a2 |fuelCons |heat |availPwr |cpuSm |maxtorqueq |bank8FM |Accel | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|REBAGG: REsampled BAGGing for Imbalanced Regression |REBAGG |✅|R |https://github.com/paobranco/REBAGG |servo |a6 |Abalone |machinecpu |a3 |a4 |a1 |a7 |boston |a2 |a5 |fuelCons |availPwr |cpuSm |maxtorqueq |dAiler |bank8FM |ConcrStr |Accel |airfoild | | | | | | | | | | | | | | | | | | | | | | |
|SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary | |❌ | |NA | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Utility-based Predictive Analytics | |❌ | | |servo |a6 |Abalone |a3 |a4 |a1 |a7 |boston |a2 |a5 |fuelCons |bank8FM |Accel |airfoild |machinecpu |availPwr |cpuSm |maxtorqueq |dAiler |ConcrStr | | | | | | | | | | | | | | | | | | | | | | |
|Learning from Imbalanced Data Sets | |❌ | |NA | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|A Study on the Impact of Data Characteristics in Imbalanced Regression Tasks | |❌ | |❌ | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Biased Resampling Strategies for Imbalanced Spatio-Temporal Forecasting | |Specific | |https://github.com/mrfoliveira/STResampling-DSAA2019/tree/master/inst/extdata |MESA |Air_Pollution |NCDC |Air_Climate |TCE |RURAL |airBase |Beijing |UrbanAir | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Imbalanced regression and extreme value prediction |SERA |✅|R |https://github.com/nunompmoniz/IRon/tree/master/data + https://lib.stat.cmu.edu/datasets/ |diabetes |triazines |a7 |elecLen1 |boston |forestFires |strikes |mortgage |treasury |musicorigin |airfoild |accel |fuelcons |availpwr |maxtorqueq |debutenizer |space.ga |pollen |abalone |wine |deltaailerons |heat |cpuAct |kinematics8fh |kinematics32fh |pumaRobot |deltaElevation |sulfur1 |sulfur2 |ailerons |elevators |calHousing|house8h |house16h| | | | | | | | |
|Improving enzyme optimum temperature prediction with resampling strategies and ensemble learning |RO ; RU ; SMOTER ; GN, ; WERCS ; REBAGG ; metrics: F1S |✅|python |https://github.com/jafetgado/resreg/blob/master/resreg/resreg.py + https://github.com/jafetgado/tomerdesign/ |Brenda | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Density‑based weighting for imbalanced regression |DenseWeight |✅|Python |https://github.com/SteiMi/denseweight + https://github.com/SteiMi/density-based-weighting-for-imbalanced-regression/tree/main/exp3/data + https://github.com/paobranco/SMOGN-LIDTA17 |a1 |a2 |a3 |a4 |a5 |a6 |a7 |Abalone |accel |Airfoild |AvailPwr |Bank8FM |Boston |ConcrStr |cpuSm |dAiler |FuelCons |MachineCpu |maxtorqueq |Servo | | | | | | | | | | | | | | | | | | | | | | |
|A novel cost-sensitive algorithm and new evaluation strategies for regression in imbalanced domains | |✅|Matlab |https://github.com/lsadouk/imbalanced_regression |Abalone |Accel |Heat |cpuSm |bank8FM |Parkinson |dAiler |H101_North_D7 |I5_South_D7 |I5_North_D7 |I210_West_D7 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Sampling To Improve Predictions For Underrepresented Observations In Imbalanced Data | |❌ | |❌ |penicillin_production | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Chebyshev approaches for imbalanced data streams regression models | |Specific | |https://github.com/ehaminian/imbalancedDataStream/tree/master/stream1 |puma32hh |cpusm |elevators |bike |energy |calhousing |gasemission |mv |fried |polution |car_price |query |GPU |3d | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|DistSMOGN: Distributed SMOGN for Imbalanced Regression Problems |DistSMOGN |✅|python |https://github.com/ndao1104/distributed-resampling |Boston |Abalone |Bank8FM |heat |cpuSM |energy |superconductivity| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Geometric SMOTE for regression | |❌ | |https://paobranco.github.io/DataSets-IR/ + https://sci2s.ugr.es/keel/datasets.php |anacalt |bank8FM |baseball |boston |compactiv |concrstr |cpuSm |ele.1 |ele.2 |forestFires |friedman |laser |machineCPU |mortgage |quake |stock |treasury |wankara | | | | | | | | | | | | | | | | | | | | | | | | |
|Model Optimization in Imbalanced Regression |SERA |✅|R |https://github.com/anibalsilva1/IRModelOptimization |diabetes |triazines |a7 |autoPrice |elecLen1 |boston |forestFires |wages |strikes |mortgage |treasury |musicorigin |airfoild |accel |fuelcons |availpwr |maxtorqueq |debutenizer |space.ga |pollen |abalone |wine |deltaailerons |heat |cpuAct |kinematics8fh|kinematics32fh |pumaRobot |deltaElevation|sulfur |ailerons |elevators |calHousing |house8h |house16h|onlineNewsPopRegr| | | | | | |
|A boosting resampling method for regression based on a conditional variational autoencoder | |❌ | |https://archive.ics.uci.edu/ + https://www.dcc.fc.up.pt/~ltorgo/DataMiningWithR/ |F1 |F2 |Abalone |Boston |dAiler |Indoor_air_quality | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Robustness Evaluation of Regression Tasks with Skewed Domain Preferences | |❌ | |❌ |Mail |activity |a1 |a7 |price |salary |length |norway |housevalue |area |wages |strikes |output |strength |yc30 |cdrate |v100 |soundpressure |accel |fuel |power |torque |violentcrimes |richter |y |lny |density |germany |beijing | | | | | | | | | | | | | |
|A Survey of Learning with Imbalanced Data, Representation Learning and SEP Forecasting | |❌ | |NA | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|ASER: Adapted squared error relevance for rare cases prediction in imbalanced regression | |❌ | |https://github.com/yingk1213/ASER |diabetes |a7 |autoPrice |elecLen1 |boston |forestFires |wages |strikes |mortgage |treasury |musicorigin |airfoild |accel |fuelcons |availpwr |maxtorqueq |debutenizer |space.ga |pollen |abalone |wine |deltaailerons|heat |cpuAct |kinematics8fh |pumaRobot |deltaElevation |sulfur |ailerons |elevators |calHousing|house8h | | | | | | | | | | |
|Imbalanced Mixed Linear Regression | |Specific | |NA | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Semi-Supervised Graph Imbalanced Regression | |Specific | |NA | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|A broad review on class imbalance learning techniques | |❌ | |NA |Wisconsin |Pima |Yeast_1 |Vehicle_2 |Vehicle_1 |Segment |Yeast_3 |Page_blocks |Yeast_2vs4 |Ecoli_0234vs5 |Yeast_0359vs78|Yeast_0256vs3789 |Ecoli_046vs5 |Ecoli_01vs235 |Yeast_05679vs4|Vowel |Ecoli_067vs5 |Led7digit_02456789vs1|Ecoli_01vs5 |Ecoli_0147vs56 |Ecoli_0146vs5 |Glass_4 |Ecoli_4 |Yeast_1458Vs7 |Glass_5 |Yeast_2Vs8 |Yeast_4 |Yeast_1289Vs7|Yeast_5 |Ecoli_0137vs26|Yeast_6 |Abalone | | | | | | | | | | |
|A Review of Machine Learning Techniques in Imbalanced Data and Future Trends | |❌ | |NA | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Enhancing soft computing techniques to actively address imbalanced regression problems | |❌ | |The selected datasets come from ‘‘Irvine Machine Learning Repository’’ (UCI) (Dua & Graff, 2017), ‘‘Knowledge Extraction based on Evolutionary Learning’’ (KEEL) (Triguero et al., 2017), ‘‘Dataset Collections of Weka’’ (WEKA) (Witten et al., 2016), ‘‘Delve Datasets’’ (DELVE) (Akujuobi & Zhang, 2017), ‘‘Luis Torgo Repository’’ (LTR) (Torgo, 2023) and from ‘‘Journal of Statistics Education Data Archive’’ (JSE) (JSE, 2023). These repositories are high quality, certified and supported by many other studies.|Abalone |Airfoild |Anacalt |Baseball |boston |concrstr |machinecpu |Electrical_Length|Electrical_Maintenance|Facebook_Measures |forestfires |laser.generated |Mortgage |AutoPrice |Quake |Servo |Strikes |Treasury |Triazines |Yacht_Hydrodynamics|Add |Ailerons |Bank32 |Bank8 |Computer_activity|California |Cpusm |deltaailerons|Deltaelv |house16h |house8h |puma32hh | | | | | | | | | | |
|Imbalanced regression using regressor‑classifier ensembles |Federated Ensemble Learning using Classification |✅|python |https://github.com/oghenejokpeme/EFERUC + https://data.mendeley.com/datasets/mpvwnhv4vb/2 |Branco |Gene_expression |OpenML |QSAR |Yeast | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Multi-output Regression for Imbalanced Data Stream | |Specific | |NA | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|tian2023unbalanced | |❌ | |NA |Abalone |Airfoild |ER_activity | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Adapting a deep convolutional RNN model with imbalanced regression loss for improved spatio-temporal forecasting of extreme wind speed events in the short to medium range| |specific |python |https://github.com/dscheepens/Deep-RNN-for-extreme-wind-speed-prediction | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Spatial-SMOTE for handling imbalance in spatial regression tasks | |❌ accessible | |https://www.kaggle.com/datasets/camnugent/california-housing-prices + https://www.kaggle.com/datasets/thedevastator/airbnb-prices-in-european-cities |california |AirBnB_prices | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|ImbalancedLearningRegression - A Python Package to Tackle the Imbalanced Regression Problem |RO ; RU; SMOTE ; GN; CNN; ENN ; ADASYN ; TOMEK |✅|python |https://github.com/paobranco/ImbalancedLearningRegression/tree/master |College |SF_Salaries |Summary_of_Weather |avocado |diabetic_data|calHousing |insurance |red_wine |weatherHistory | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Data Augmentation for Imbalanced Regression | |Specific | |http://www2.math.uconn.edu/~valdez/data.html |telematics | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Imbalance in Regression Datasets | |❌ | |NA | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Oversampling Techniques for Imbalanced Data in Regression | |❌ | |NA |ANACALT |bank8FM |baseball |boston |compactiv |concrstr |cpuSm |ele.1 |ele.2 |forestFires |friedman |laser |machineCPU |mortgage |quake |stock |treasury |wankara | | | | | | | | | | | | | | | | | | | | | | | | |
|Resampling strategies for imbalanced regression: a survey and empirical analysis |SMOTE ; RO ; RU ; GN ; SMOGN ; WERCS ; Metric: F1S + SERA |✅|python |https://github.com/JusciAvelino/imbalancedRegression/tree/main/ |wine |anacalt |meta |cocomo.numeric|Abalone |a3 |forestFires |a1 |a7 |boston |pdgfr |sensory |a2 |kdd.coil.1 |triazines |airfoild |treasury |mortgage |debutenizer |fuelCons |heat |california |AvailPwr |compactiv |cpuSm |maxtorqueq |lungcancer.shedden|space.ga |ConcrStr |Accel | | | | | | | | | | | | |
|A survey on imbalanced learning: latest research, applications and future directions | |❌ | |NA | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Research on Imbalanced Data Regression Based on Confrontation | |❌ | |NA |Airfoild |Abalone |Yacht.Hydrodynamics |concrstr | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|A novel gradient boosting approach for imbalanced regression |IMr-GB |✅|python |https://github.com/vengozhang/IMr-GB |a1 |Abalone |Accel |availPwr |bank8FM |boston |ConcrStr |cpuSm |fuelCons |maxtorqueq | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Affine combination-based over-sampling for imbalanced regression | |❌ | |https://github.com/lzz185/ACOS |heat |airfoild |availpwr |ele.2 |laser |maxtorqueq |mortgage |pendigits |qsar.aquatic.toxicity |quake |sulfur |yacht.hydrodynamics|calHousing |a7 |baseball | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Rare event prediction in imbalanced regression with adaptive weighted support vector regression | |❌ | |http://www.ics.uci.edu/ mlearn/ + https://sci2s.ugr.es/keel/datasets.php + https://github.com/nunompmoniz/Iron |a1 |wages |qsar.aquatic.toxicity |strikes |grison |qsar.fish.toxicity |laser |airfoild |wine |accel |fuelcons |availpwr |abalone |wine |wine |kinematics8fh |sulfur |house8h |house16h |calHousing |onlineNewsPopRegr|mv |fried | | | | | | | | | | | | | | | | | | | |
|Sparse feature selection and rare value prediction in imbalanced regression | |❌ | |https://github.com/guanying24/SerEnet |diabetes |AutoPrice |elecLen1 |strikes |mortgage |treasury |musicOrigin |space.ga |pollen |abalone |deltaailerons |heat |kinematics8fh |kinematics32fh |deltaElevation|ailerons |elevators |OnlineNewsPopRegr | | | | | | | | | | | | | | | | | | | | | | | | |
|WSMOTER: a novel approach for imbalanced regression |WSMOTER |✅|python |https://drive.google.com/drive/folders/1h6Q5sKB5bnqk0Kh01sH1uc6LTp4KSPbb |a1 |a2 |a3 |a4 |a5 |a6 |a7 |Abalone |accel |Ailerons |Airfoild |AutoPrice |availpwr |Bank8FM |Boston |California |Compactiv |concrstr |cpuAct |cpuSm |deltaailerons |Deltaelv |ele.1 |elevators |ForestFires |fuelcons |Heat |House |Kinematics32fh|MachineCpu |maxtorqueq|Mortgage |puma32hh |Servo |Treasury|Wankara | | | | | | |
|Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory: Application in Regression |GOLIATH |✅|R |http:local |NO2 |Boston |cpuSm |bank8fm |abalone | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Data Augmentation with Variational Autoencoder for Imbalanced Dataset |DAVID |✅|python |https://github.com/sstocksieker/DAVID/ |bank8FM |abalone |boston |NO2 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Boarding for ISS: Imbalanced Self-Supervised: Discovery of a Scaled Autoencoder for Mixed Tabular Datasets | |Specific | |NA | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|A Selective Under-Sampling (SUS) Method For Imbalanced Regression | |❌ | |NA |Abalone |Accel |a1 |a2 |a3 |a4 |a5 |a6 |a7 |availPwr |bank8FM |boston |cpuSm |fuelCons |heat |maxtorqueq | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Error Distribution Smoothing: Advancing Low-Dimensional Imbalanced Regression |EDS |✅|python |https://a❌ymous.4open.science/r/Error-Distribution-Smoothing-762F/README.md |quadcopter dynamics |Cartpole | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|KNNOR-Reg: A python package for oversampling in imbalanced regression |KNNOR-Reg: A python package for oversampling in imbalanced regression |✅|python |https://github.com/ashhadulislam/augmentdatalib_reg_source/blob/main/README.md |mortgage | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Uncertainty quantification driven machine learning for improving model accuracy in imbalanced regression tasks |UQDIR |✅|python |https://github.com/tubadolar/uqdir/tree/main/datasets |accel |abalone |bank8fm |boston |cpusm |ailerons |elevators |earthquake |California |delta | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|Quantification of Data Imbalance |Imb_quanti |✅|python |https://github.com/OFFIS-ROC/imbaqu |Energy.efficiency |forestfires |Optical.interconnection.network|concrstr |Servo |Combined.cycle.power.plant|Grid.stability |superconductivity|Synchronous.machine |Auction.verification|Airfoild |Concrete.slump.test|traffic.behaviour|Yacht.hydrodynamics|Fish.toxicity |Wave.energy.perth.49|Aquatic.toxicity|Steel.industry |Computer.hardware|Abalone |Age.prediction |Parkinson |Winequality.white|Facebook.metrics|Ailerons |Anacalt |Autoprice |bank32 |bank8 |baseball |boston |California|deltaailerons|Deltaelv|ele.1 |ele.2 |house16h|Laser.generated|mortgage|puma32hh|Strikes|Treasury|
|An Investigation of Imbalanced Regression Loss Functions with Neural Network Models |Scaled-Weighted loss |✅|python |https://drive.google.com/drive/folders/1zOHx_BwZL45RnTWCMt3VNZ6bgMugLNQk |Simple_Ocean_Data_Assimilation | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
## Sources
These datasets have been collected from public repositories such as **UCI**, **Kaggle**, and various **GitHub pages** associated with prior research on imbalanced regression.
Below is a list of the main source repositories used to compile this collection:
- [https://archive.ics.uci.edu](https://archive.ics.uci.edu)
- [https://www.kaggle.com](https://www.kaggle.com)
- https://sci2s.ugr.es/keel/datasets.php
- https://www.dcc.fc.up.pt/~ltorgo/EPIA2013/
- https://www.dcc.fc.up.pt/~ltorgo/Regression/DataSets.html
- https://lib.stat.cmu.edu/datasets/
- https://github.com/paobranco/UtilityOptimizationRegression
- https://github.com/nunompmoniz/Ensembles_LIDTA2017
- https://github.com/paobranco/SMOGN-LIDTA17
- https://github.com/paobranco/NeighborhoodBiasResamplingRegression
- https://github.com/nunompmoniz/DSAA2018
- https://github.com/paobranco/Pre-processingApproachesImbalanceRegression
- https://paobranco.github.io/DataSets-IR/
- https://github.com/paobranco/REBAGG
- https://github.com/mrfoliveira/STResampling-DSAA2019/tree/master/inst/extdata
- https://github.com/nunompmoniz/IRon/tree/master/data
- https://github.com/SteiMi/denseweight
- https://github.com/SteiMi/density-based-weighting-for-imbalanced-regression/tree/main/exp3/data
- https://github.com/lsadouk/imbalanced_regression
- https://github.com/ndao1104/distributed-resampling
- https://paobranco.github.io/DataSets-IR/
- https://github.com/anibalsilva1/IRModelOptimization
- https://github.com/yingk1213/ASER
- https://data.mendeley.com/datasets/mpvwnhv4vb/2
- https://github.com/paobranco/ImbalancedLearningRegression/tree/master
- http://www2.math.uconn.edu/~valdez/data.html
- https://github.com/JusciAvelino/imbalancedRegression/tree/main/
- https://github.com/vengozhang/IMr-GB
- https://github.com/lzz185/ACOS
- https://github.com/guanying24/SerEnet
- https://drive.google.com/drive/folders/1h6Q5sKB5bnqk0Kh01sH1uc6LTp4KSPbb
- https://github.com/sstocksieker/DAVID/
- https://github.com/tubadolar/uqdir/tree/main/datasets
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license: cc-by-4.0
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