Principal Components Analysis: An Alternative Way for Removing Natural Growth Trends

Silva, Daniela Oliveira Da; Klausner, Virginia; Prestes, A.; Macedo, Humberto Gimenes; Aakala, Tuomas; Silva, Iuri Rojahn da

doi:10.1007/s00024-021-02776-1

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…In this paper, this average linkage clustering method was applied hierarchical clustering, and after that the features of principal component analysis were added to reduce the dimensionality and increase interpretability based on the mathematical formula of it which is introduced in [24,25].…”

Section: Data Preparationmentioning

confidence: 99%

Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

Aly¹,

Hussein²,

Rahouma³

2022

Computers, Materials &Amp; Continua

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This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees' performance. The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance. The work of this paper consists of two parts. The first part is based on collecting data of employees to calculate and illustrate the performance of each employee. The second part is based on the classification and prediction techniques of the employee performance. This model is designed to help companies in their decisions about the employees' performance. The classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the features. Results of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming years. Results also show that the Grasshopper Optimization, followed by "KF" with the Gradient Boosting Tree as classifier and predictor, is characterized by a high accuracy. The proposed algorithm is compared with other known techniques where our results are fund to be superior.

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Section: Data Preparationmentioning

confidence: 99%

Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

Aly¹,

Hussein²,

Rahouma³

2022

Computers, Materials &Amp; Continua

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“…This research uses the random forest algorithm of machine learning to extract the poor counties from nighttime light remote sensing images [35,36]. Random Forest Algorithm is based on Bagging algorithm.…”

Section: Random Forest Classification Algorithmmentioning

confidence: 99%

Big Data Spatio-Temporal Correlation Analysis and LRIM Model Based Targeted Poverty Alleviation through Education

Han

Liu

Sui

et al. 2021

IJGI

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There are many factors affecting poverty, among which education is an important one. Firstly, from the perspective of digital statistics, this research quantitatively analyzes the correlation between average education years (AEY) and Gross Domestic Product per capita (GDP/C), and finds that there is a significant positive correlation between AEY and GDP/C in provinces of China. Furthermore, from the perspective of spatial distribution and geostatistics, this research analyzes the correlation between AEY and the distribution of poor counties, revealing the inherent connection between education and poverty. Based on the data processing of nighttime light remote sensing images, this research adopts the machine learning method of random forest to extract the distribution status of spatio-temporal sequences for poor counties. Through the analysis, it is found that poor counties are characterized by centralized distribution and spatial autocorrelation spatially, and the number of poor counties decreases year by year in temporal evolution. On this basis, we analyze the correlation between education levels and the distribution of poor counties. It is found that, on the spatial scale, AEY in poor counties is relatively low, while AEY in non-poor counties is relatively high, showing a significant negative correlation between the two. On the temporal scale, the number of poor counties gradually decreased from 2000 to 2010, and at the same time, the education levels of poor counties also gradually improved. Finally, from the perspective of improving education levels to promote poverty elimination, we analyze the main factors affecting education using Principal Component Analysis (PCA) and other methods and obtain a regression model. This research proposes the Linear and Residual Integration Model (LRIM) to more accurately predict AEY in each province in 2020 based on historical data, and identifies the regions with low AEY as key regions for targeted poverty alleviation through education (TPAE) in the future. This research provides a decision-making basis to achieve TPAE means, helping to achieve the victory of the national education poverty elimination battle.

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Ocean–atmosphere interaction identified in tree-ring time series from southern Brazil using cross-wavelet analysis

Muraja,

Klausner,

Prestes

et al. 2023

Theor Appl Climatol

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Principal Components Analysis: An Alternative Way for Removing Natural Growth Trends

Cited by 3 publications

References 23 publications

Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

Big Data Spatio-Temporal Correlation Analysis and LRIM Model Based Targeted Poverty Alleviation through Education

Ocean–atmosphere interaction identified in tree-ring time series from southern Brazil using cross-wavelet analysis

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