Linear regression

Hope, Thomas M.H.

doi:10.1016/b978-0-12-815739-8.00004-3

Cited by 61 publications

(36 citation statements)

References 17 publications

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“…Linear Regression is a method to get a linear model as much as possible to accurately predict the real value output label. In Linear regression, the least square function is used to establish the relationship between one or more independent variables ( 22 ). An easy and commonly used supervised learning method is k NN.…”

Section: Methodsmentioning

confidence: 99%

A comparative study of gastric histopathology sub-size image classification: From linear regression to visual transformer

Chen

Liu

et al. 2022

Front. Med.

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IntroductionGastric cancer is the fifth most common cancer in the world. At the same time, it is also the fourth most deadly cancer. Early detection of cancer exists as a guide for the treatment of gastric cancer. Nowadays, computer technology has advanced rapidly to assist physicians in the diagnosis of pathological pictures of gastric cancer. Ensemble learning is a way to improve the accuracy of algorithms, and finding multiple learning models with complementarity types is the basis of ensemble learning. Therefore, this paper compares the performance of multiple algorithms in anticipation of applying ensemble learning to a practical gastric cancer classification problem.MethodsThe complementarity of sub-size pathology image classifiers when machine performance is insufficient is explored in this experimental platform. We choose seven classical machine learning classifiers and four deep learning classifiers for classification experiments on the GasHisSDB database. Among them, classical machine learning algorithms extract five different image virtual features to match multiple classifier algorithms. For deep learning, we choose three convolutional neural network classifiers. In addition, we also choose a novel Transformer-based classifier.ResultsThe experimental platform, in which a large number of classical machine learning and deep learning methods are performed, demonstrates that there are differences in the performance of different classifiers on GasHisSDB. Classical machine learning models exist for classifiers that classify Abnormal categories very well, while classifiers that excel in classifying Normal categories also exist. Deep learning models also exist with multiple models that can be complementarity.DiscussionSuitable classifiers are selected for ensemble learning, when machine performance is insufficient. This experimental platform demonstrates that multiple classifiers are indeed complementarity and can improve the efficiency of ensemble learning. This can better assist doctors in diagnosis, improve the detection of gastric cancer, and increase the cure rate.

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Section: Methodsmentioning

confidence: 99%

A comparative study of gastric histopathology sub-size image classification: From linear regression to visual transformer

Chen

Liu

et al. 2022

Front. Med.

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“…Multiple-variable linear regression is a fundamental technique in ML used to predict a continuous target variable by modeling its relationship with one or more independent variables, also known as features [28]. It is a supervised learning approach that involves training a model on a dataset consisting of labeled samples, where each sample includes the target variable and a set of features that describe the input.…”

Section: Modelsmentioning

confidence: 99%

Machine Learning-Enhanced Modelling of Dual-Core Silver-Coated PCF-SPR Sensor for Analyte Sensing

Haque,

Saha,

Islam

et al. 2024

Preprint

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Photonic crystal fiber (PCF)-based surface plasmon resonance (SPR) sensors have garnered significant attention for their remarkable sensitivity in detecting molecular interactions and bio-molecular binding events. This study has proposed an approach to enhance the sensitivity of dual-core silver-coated PCF-SPR sensors and modelling by integrating machine learning (ML) algorithms. This sensor stands out with its dual-core design, boasting impressive capabilities such as the highest wavelength sensitivity of 30000 nm/RIU and a 3.33×10-6 resolution. It also covers a wide refractive index (RI) range from 1.34 to 1.41, using silver as the plasmonic material. Additionally, it utilizes ML techniques, including support vector regression, random forests, and multiple-variable linear regression, to enhance modelling accuracy, particularly in predicting confinement loss and the real part of effective RI. The high sensitivity of the proposed PCF-SPR sensor makes it a strong contender for use in bio-sensing applications.

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“…Regression is one of many statistical methods that serve to see the pattern of the relationship between the response variable and predictor variable [30].…”

Section: B Linear Regressionmentioning

confidence: 99%

Long-term forecasting for growth of electricity load based on customer sectors

Sujito

Hadi

Gumilar

et al. 2022

J. Mechatron. Electr. Power Veh. Technol.

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The availability of electrical energy is an important issue. Along with the growth of the human population, electrical energy also increases. This study addresses problems in the operation of the electric power system. One of the problems that occur is the power imbalance due to scale growth between demand and generation. Alternative countermeasures that can be done are to prepare for the possibility that will occur in the future or what we are familiar with forecasting. Forecasting using the multiple linear regression method with this research variable assumes the household sector, business, industry, and public sectors, and is considered by the influence of population, gross regional domestic product, and District Minimum Wage. In forecasting, it is necessary to evaluate the accuracy using mean absolute percentage error (MAPE). MAPE evaluation results show a value of 0.142 % in the household sector, 0.085 % in the business sector, 1.983 % in the industrial sector, and 0.131 % in the total customer sector.

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Linear regression

Cited by 61 publications

References 17 publications

A comparative study of gastric histopathology sub-size image classification: From linear regression to visual transformer

A comparative study of gastric histopathology sub-size image classification: From linear regression to visual transformer

Machine Learning-Enhanced Modelling of Dual-Core Silver-Coated PCF-SPR Sensor for Analyte Sensing

Long-term forecasting for growth of electricity load based on customer sectors

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