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Regression Method in Data Mining: A Systematic Literature Review

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Abstract

Regression is one of the most important supervised learning methods in data mining that is used to predict and discover knowledge in data mining science. After reviewing the studies conducted in the field of regression, it has been found that the tendency to use this method is increasing day by day among researchers. This study reviews 500 articles from about 230 reputable journals under one framework over the twenty-first century and also discusses the status and use of regression in data mining research. The systematic framework presented in this study includes the following steps: 1—Examining the position of regression in research conducted in data mining and determining the trend of different journals to conduct research in the field of regression in different years 2—Examining different study areas in the field of regression and determining the trend to conduct research in various areas of study in different years 3—Examining the algorithms used in the field of regression and determining the most widely used and trend to use algorithms by researchers in different years 4—Examining the keywords used in regression research in data mining and determining the strongest and most attractive rules obtained from the relationships of these keywords with each other using the Apriori algorithm.

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Abbreviations

RT:

Regression tree

CART:

Classification and regression tree

MLR:

Multiple logistic regression

RF:

Random forest

SVR:

Support vector regression

HABES:

Harmful algal bloom expert system

ANN:

Artificial neural network

BRT:

Boosted regression tree

ZOIB:

Zero-or-one inflated beta

LLM:

Logit leaf model

TPOT:

Tree-based pipeline optimization tool

GBT:

Gradient boosting tree

MLPNN:

Multilayer perceptron neural network

TGD:

Three gorges dam

LSSVM:

Least squares support vector machine

DT:

Decision tree

DNN:

Dynamic neural network

PCA:

Principal component analysis

CSS:

Customer satisfaction survey

HPSO:

Hybrid particle swarm optimization

HHCART:

CART using house holders matrices

FT-SVR:

Fourier transform and SVR

ROC:

Receiver operating characteristic

CHAID:

Chi-square automatic interaction detection

NLRA:

Non-linear regression analysis

KNN:

K-nearest neighbor

HTBR:

Hierarchical tree-based regression

ISM-RT:

Interpretative structural modeling with regression tree

GBR:

Gradient boosted regression

LSTM:

Long-short term memory network

ABC-LR:

Artificial bee colony with logistic regression

GDP:

Gross domestic product

X12-ARIMA:

X12-auto regressive integrated moving average

MLP:

Multilayer perceptron

MARS-GBM:

MARS and gradient boosting machine

LOR:

Logistic regression

LR:

Linear regression

MRT:

Multivariate regression tree

SVM:

Support vector machine

GMDH:

Group method of data handling

STR:

Smooth transition regression

HPSO:

Hybrid particle swarm optimization

SPFs:

Safety performance functions

LDA:

Linear discriminant analysis

CT:

Classification tree

MARS:

Multivariate adaptive regression spline

ANFIS:

Adaptive neuro-fuzzy inference systems

IVTS:

Interval-valued time series

XGBoost:

Extreme gradient boosting

GBDT:

Gradient boosted decision tree

LSSVR:

Least squares support vector regression

QSAR:

Quantitative structure activity relationship

STR-tree:

Smooth transition regression and CART

RBFN:

Radial basis function network

HPSORTRBFN:

HPSO, RT and RBFN

SDM:

Species distribution model

MS-HCA:

Multidimensional scaling and hierarchical cluster analysis

AUC:

Area under the curve

IOT:

Internet of things

DTR:

Decision tree regression

GBRT:

Gradient boosting regression tree

ELM:

Extreme learning machine

fLogSLFN:

Filtered logistic single-hidden layer feedforward neural network

RFR:

Random forest regression

EMD-LSTM:

Empirical mode decomposition-based LSTM

ICD-9-CM:

International classification of disease 9th—revision clinical modification

SO2:

Sulfur dioxide

GP:

Genetic programming

GBDT-LR:

Gradient boosting decision tree with logistic regression

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  1. Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran

    Mohammad Vahid Sebt, Yaser Sadati-Keneti, Misagh Rahbari, Zohreh Gholipour & Hamid Mehri

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  1. Mohammad Vahid Sebt
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Sebt, M.V., Sadati-Keneti, Y., Rahbari, M. et al. Regression Method in Data Mining: A Systematic Literature Review. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-024-10088-5

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