samgohan/Tabular_Imbalanced_Regression

Tabular Imbalanced Regression Datasets

Repository Summary

This repository provides a collection of 81 tabular datasets curated for research on 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:

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: Python function to compute the imbalance ratio.
• Demo Notebook: 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
• 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_ratio	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://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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